Image transmission method, device and equipment, readable storage medium and program product

Abstract

The invention provides an image transmission method and device, equipment, a readable storage medium and a program product, and relates to the technical field of data security, and the method applied to first equipment comprises the steps: carrying out the multi-scale transformation and matrix decomposition processing of a carrier image, and obtaining a first feature matrix of a first region in the carrier image, the first area is used for embedding a watermark image; performing compression encryption and matrix decomposition processing on the watermark image to obtain a second feature matrix of the watermark image; according to the first feature matrix, the second feature matrix and a pre-configured embedding factor, embedding the watermark image into the first region of the carrier image to obtain a target image; performing compressed sensing encryption processing on the target image to generate an encrypted image; and sending the encrypted image to a cloud server. According to the embodiment of the invention, the watermark embedding efficiency is improved, the robustness of the watermark image is enhanced, and secure transmission is realized.

Description

Technical Field

[0001] This invention relates to the field of data security technology, and in particular to an image transmission method, apparatus, device, readable storage medium, and program product. Background Technology

[0002] Since the beginning of the 21st century, the widespread application of communication and information technology and the Internet has brought with it the urgent problem of digital products being copied, stolen, and counterfeited. The protection of digital information has become particularly important, leading to the emergence of digital watermarking technology. Digital watermarking technology embeds specific watermarks into digital products, possessing characteristics such as invisibility, robustness, and detectability. This aims to protect the copyright and integrity of digital products, as well as prevent copying, thus protecting digital media content from piracy and tampering. However, with continuous technological advancements, digital watermarking technology faces increasing challenges, the most significant being resistance to geometric attacks and image processing attacks. Resistance to geometric attacks means that even after geometric transformations such as rotation, scaling, translation, or cropping, the digital watermark can still be accurately extracted. Resistance to image processing attacks means that even after certain image processing operations (such as smoothing, noise reduction, and compression) are applied to the image in which the watermark is embedded, the digital watermark can still be accurately extracted.

[0003] Current digital watermarking embedding schemes generally adopt the method of directly embedding the watermark into the carrier image. However, this method has the disadvantages of low watermark embedding efficiency and insufficient robustness of digital watermarks. Due to their transformation characteristics, they lack certain geometric invariances, thus still having fatal flaws when facing various geometric attacks.

[0004] Currently, image security sharing schemes based on compressed sensing are mainly designed for situations where both parties have their own independent keys. Key negotiation is required during sharing, which can lead to key leakage for each user. In scenarios where each user is reliable, multiple key negotiations are required for image sharing among multiple users, which hinders the efficiency of secure image sharing among multiple parties. Summary of the Invention

[0005] The purpose of this invention is to provide an image transmission method, apparatus, device, readable storage medium, and program product to solve the problems of low watermark embedding efficiency, insufficient robustness of digital watermarks, and low image transmission security.

[0006] To address the aforementioned technical problems, embodiments of the present invention provide an image transmission method, applied to a first device, comprising:

[0007] The carrier image is subjected to multi-scale transformation and matrix decomposition to obtain the first feature matrix of the first region in the carrier image, wherein the first region is the region used to embed the watermark image.

[0008] The watermark image is compressed, encrypted, and matrix decomposed to obtain the second feature matrix of the watermark image;

[0009] Based on the first feature matrix, the second feature matrix, and the pre-configured embedding factor, the watermark image is embedded into the first region of the carrier image to obtain the target image;

[0010] The target image is subjected to compressed sensing encryption processing to generate an encrypted image;

[0011] The encrypted image is sent to the cloud server.

[0012] Optionally, the step of performing multi-scale transformation and matrix decomposition on the carrier image to obtain the first feature matrix of the first region in the carrier image includes:

[0013] Based on the dual-tree complex wavelet transform technique, the carrier image is processed by multi-scale transformation to obtain multiple frequency bands;

[0014] Based on the target frequency band among the multiple frequency bands, a first region in the carrier image is determined;

[0015] Based on the discrete cosine transform and singular value decomposition techniques, the first region is subjected to matrix decomposition to obtain the first feature matrix of the first region.

[0016] Optionally, determining the first region in the carrier image based on the target frequency band among the plurality of frequency bands includes:

[0017] Based on the characteristics of the human eye and the frequency of the frequency band, the frequency band is filtered, and the frequency band with the lowest frequency is determined as the target frequency band;

[0018] The region corresponding to the target frequency band is determined as the first region in the carrier image.

[0019] Optionally, the step of performing matrix decomposition processing on the first region according to discrete cosine transform and singular value decomposition techniques to obtain the first feature matrix of the first region includes:

[0020] The first region is divided into blocks to obtain multiple first region blocks;

[0021] Perform discrete cosine transform on each of the first region blocks to obtain the frequency domain coefficient matrix;

[0022] Select the mid-frequency coefficients from the frequency domain coefficient matrix to construct the sparse matrix of the first region;

[0023] The sparse matrix is ​​subjected to singular value decomposition to obtain the first feature matrix of the first region.

[0024] Optionally, the step of compressing, encrypting, and decomposing the watermark image to obtain a second feature matrix of the watermark image includes:

[0025] Based on the first key, the watermark image is sequentially subjected to compressed sensing sampling and chaotic encryption processing to obtain an encrypted watermark image, wherein the first key is randomly generated based on a chaotic sequence;

[0026] The encrypted watermark image is subjected to matrix decomposition processing based on discrete cosine transform and singular value decomposition techniques to obtain the second feature matrix of the encrypted watermark image.

[0027] Optionally, the step of embedding the watermark image into the first region of the carrier image based on the first feature matrix, the second feature matrix, and a pre-configured embedding factor to obtain the target image includes:

[0028] The third feature matrix is ​​obtained by calculating the second feature matrix into the first feature matrix based on the first feature matrix, the second feature matrix, and the pre-configured embedding factor.

[0029] The third feature matrix is ​​subjected to inverse matrix decomposition and multi-scale inverse transformation to obtain the target region, wherein the target region is the first region embedded in the watermark image;

[0030] The carrier image is reconstructed based on the target region to obtain the target image.

[0031] Optionally, performing compressed sensing encryption processing on the target image to generate an encrypted image includes:

[0032] The target image is compressed sensing encryption using a first key to generate an encrypted image, wherein the first key is randomly generated based on a chaotic sequence.

[0033] This invention also provides an image transmission method applied to a second device, comprising:

[0034] Obtain an encrypted image sent by a cloud server, wherein the encrypted image is an encrypted target image, and the target image embeds an encrypted watermark image;

[0035] The encrypted image is decrypted and reconstructed to obtain the target image;

[0036] The target image is subjected to multi-scale transformation and matrix decomposition to obtain the third feature matrix of the target image;

[0037] Based on the pre-configured embedding factor, the encrypted watermark image is extracted from the third feature matrix to obtain the second feature matrix corresponding to the watermark image;

[0038] The second feature matrix is ​​subjected to matrix inverse decomposition and decryption reconstruction to obtain the watermark image.

[0039] Optionally, the method further includes:

[0040] Send a query request to the cloud server, the query request being used to request the acquisition of the encrypted image transmitted by the first device;

[0041] The step of obtaining the encrypted image sent by the cloud server includes:

[0042] Receive the encrypted image sent by the cloud server in accordance with the query request.

[0043] Optionally, the step of decrypting and reconstructing the encrypted image to obtain the target image includes:

[0044] The encrypted image is decrypted and reconstructed using a second key to obtain the target image. The second key is randomly generated based on a chaotic sequence.

[0045] Optionally, the step of performing multi-scale transformation and matrix decomposition on the target image to obtain the third feature matrix of the target image includes:

[0046] The target image is subjected to multi-scale transformation processing based on the dual-tree complex wavelet transform technique to obtain multiple frequency bands;

[0047] Based on the target frequency band among the multiple frequency bands, a target region in the target image is determined, wherein the target region is the region embedded in the watermark image;

[0048] The target region is subjected to matrix decomposition based on discrete cosine transform and singular value decomposition techniques to obtain the third characteristic matrix of the target region.

[0049] Optionally, determining the target region in the target image based on the target frequency band among the plurality of frequency bands includes:

[0050] Based on the characteristics of the human eye and the frequency of the frequency band, the frequency band is filtered, and the frequency band with the lowest frequency is determined as the target frequency band;

[0051] The region corresponding to the target frequency band is determined as the target region in the target image.

[0052] Optionally, the step of performing matrix decomposition on the target region using discrete cosine transform and singular value decomposition techniques to obtain the third feature matrix of the target region includes:

[0053] The target region is divided into blocks to obtain multiple target region blocks;

[0054] Perform discrete cosine transform on each of the target regions to obtain the frequency domain coefficient matrix;

[0055] Select the mid-frequency coefficients from the frequency domain coefficient matrix to construct the sparse matrix of the target region;

[0056] The sparse matrix is ​​subjected to singular value decomposition to obtain the third feature matrix of the target region.

[0057] Optionally, the step of performing matrix inverse decomposition and decryption reconstruction on the second feature matrix to obtain the watermark image includes:

[0058] Perform matrix inverse decomposition on the second feature matrix to obtain the encrypted watermark image;

[0059] Based on the second key, the encrypted watermark image is sequentially subjected to chaotic decryption and compressed sensing recovery processing to obtain the watermark image, wherein the second key is randomly generated based on a chaotic sequence.

[0060] This invention also provides an image transmission method applied to a cloud server, comprising:

[0061] Receive the first encrypted image sent by the first device;

[0062] Based on the first conversion key corresponding to the first device, the first encrypted image is encrypted and converted to obtain a second encrypted image;

[0063] Receive a query request sent by a second device, the query request being used to request the acquisition of an encrypted image transmitted by the first device;

[0064] Based on the query request, the second encrypted image is encrypted and converted according to the second conversion key corresponding to the second device to obtain a third encrypted image;

[0065] The third encrypted image is sent to the second device, wherein the second device is capable of decrypting the third encrypted image using the second key of the second device.

[0066] Optionally, the method further includes:

[0067] Obtain a first conversion key, wherein the first conversion key includes a first matrix, the first matrix is ​​randomly generated according to a chaotic sequence, and the first matrix is ​​used to generate a first key, the first key is used to encrypt and generate the first encrypted image;

[0068] Obtain a second conversion key, wherein the second conversion key includes a second matrix, the second matrix is ​​randomly generated according to a chaotic sequence, and the second matrix is ​​used to generate a second key, the second key being used to decrypt the third encrypted image.

[0069] This invention also provides a first network device, including a transceiver and a processor, wherein,

[0070] The processor is used for:

[0071] The carrier image is subjected to multi-scale transformation and matrix decomposition to obtain the first feature matrix of the first region in the carrier image, wherein the first region is the region used to embed the watermark image.

[0072] The watermark image is compressed, encrypted, and matrix decomposed to obtain the second feature matrix of the watermark image;

[0073] Based on the first feature matrix, the second feature matrix, and the pre-configured embedding factor, the watermark image is embedded into the first region of the carrier image to obtain the target image;

[0074] The target image is subjected to compressed sensing encryption processing to generate an encrypted image;

[0075] The transceiver is used for:

[0076] The encrypted image is sent to the cloud server.

[0077] This invention also provides a second network device, including a transceiver and a processor, wherein,

[0078] The transceiver is used for:

[0079] Obtain an encrypted image sent by a cloud server, wherein the encrypted image is an encrypted target image, and the target image embeds an encrypted watermark image;

[0080] The processor is used for:

[0081] The encrypted image is decrypted and reconstructed to obtain the target image;

[0082] The target image is subjected to multi-scale transformation and matrix decomposition to obtain the third feature matrix of the target image;

[0083] Based on the pre-configured embedding factor, the encrypted watermark image is extracted from the third feature matrix to obtain the second feature matrix corresponding to the watermark image;

[0084] The second feature matrix is ​​subjected to matrix inverse decomposition and decryption reconstruction to obtain the watermark image.

[0085] This invention also provides a third network device, including a transceiver and a processor, wherein,

[0086] The transceiver is used to receive the first encrypted image sent by the first device;

[0087] The processor is used to perform encryption conversion on the first encrypted image according to the first conversion key corresponding to the first device to obtain a second encrypted image;

[0088] The transceiver is also used to receive a query request sent by the second device, the query request being used to request the acquisition of an encrypted image transmitted by the first device;

[0089] The processor is further configured to, based on the query request and according to the second conversion key corresponding to the second device, perform encryption conversion on the second encrypted image to obtain a third encrypted image;

[0090] The transceiver is also used to send the third encrypted image to the second device, wherein the second device is able to decrypt the third encrypted image using the second key of the second device.

[0091] This invention also provides an image transmission device, applied to a first device, comprising:

[0092] The first processing module is used to perform multi-scale transformation and matrix decomposition processing on the carrier image to obtain the first feature matrix of the first region in the carrier image, wherein the first region is the region used to embed the watermark image.

[0093] The second processing module is used to compress, encrypt, and decompose the watermark image to obtain the second feature matrix of the watermark image.

[0094] The first embedding module is used to embed the watermark image into the first region of the carrier image according to the first feature matrix, the second feature matrix and the pre-configured embedding factor to obtain the target image;

[0095] The first encryption module is used to perform compressed sensing encryption processing on the target image to generate an encrypted image;

[0096] The first sending module is used to send the encrypted image to the cloud server.

[0097] This invention also provides an image transmission device, applied to a second device, comprising:

[0098] The first acquisition module is used to acquire an encrypted image sent by the cloud server, wherein the encrypted image is an encrypted target image and the target image embeds an encrypted watermark image;

[0099] The first decryption module is used to decrypt and reconstruct the encrypted image to obtain the target image;

[0100] The third processing module is used to perform multi-scale transformation and matrix decomposition on the target image to obtain the third feature matrix of the target image;

[0101] The first extraction module is used to extract the encrypted watermark image from the third feature matrix according to the pre-configured embedding factor, and obtain the second feature matrix corresponding to the watermark image.

[0102] The fourth processing module is used to perform matrix inverse decomposition and decryption reconstruction on the second feature matrix to obtain the watermark image.

[0103] This invention also provides an image transmission device applied to a cloud server, comprising:

[0104] The first receiving module is used to receive the first encrypted image sent by the first device;

[0105] The first conversion module is used to perform encryption conversion on the first encrypted image according to the first conversion key corresponding to the first device to obtain the second encrypted image;

[0106] The second receiving module is used to receive a query request sent by the second device, the query request being used to request the acquisition of an encrypted image transmitted by the first device;

[0107] The second conversion module is used to perform encryption conversion on the second encrypted image based on the query request and the second conversion key corresponding to the second device to obtain a third encrypted image;

[0108] The third sending module is used to send the third encrypted image to the second device, wherein the second device is capable of decrypting the third encrypted image using the second key of the second device.

[0109] This invention also provides a network device, including: a processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the image transmission method as described in any of the preceding embodiments.

[0110] This invention also provides a readable storage medium, comprising: a program stored on the readable storage medium, wherein the program, when executed by a processor, implements the steps of the image transmission method as described in any of the preceding claims.

[0111] This invention also provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the image transmission method as described in any of the preceding claims.

[0112] At least one of the above technical solutions of the present invention has the following beneficial effects:

[0113] The above scheme provides an image transmission method applied to a first device, comprising: first, performing multi-scale transformation and matrix decomposition on the carrier image to obtain a first feature matrix of a first region in the carrier image, wherein the first region is the region used to embed the watermark image; then, performing compression encryption and matrix decomposition on the watermark image to obtain a second feature matrix of the watermark image; second, embedding the watermark image into the first region of the carrier image according to the first feature matrix, the second feature matrix, and a pre-configured embedding factor to obtain a target image; finally, performing compressed sensing encryption on the target image to generate an encrypted image; and sending the encrypted image to the cloud server. The above scheme embeds the watermark in the image before transmission. Specifically, the watermark image to be embedded is compressed and encrypted, and the selected first region is embedded, effectively reducing the amount of embedded data and ensuring data security, thus improving watermark embedding efficiency. Furthermore, by embedding the watermark image in different transform domains, the robustness of the watermark image to geometric attacks is enhanced. During image transmission, the cloud server acts as a relay station, and the first device uploads the encrypted data to the cloud server, achieving secure data transmission.

[0114] The image transmission method applied to the second device includes: First, acquiring an encrypted image sent by a cloud server, wherein the encrypted image is an encrypted target image, and an encrypted watermark image is embedded in the target image; then, decrypting and reconstructing the encrypted image to obtain the target image; next, performing multi-scale transformation and matrix factorization on the target image to obtain the third feature matrix of the target image; second, extracting the encrypted watermark image from the third feature matrix according to a pre-configured embedding factor to obtain the second feature matrix corresponding to the watermark image; finally, performing inverse matrix factorization and decryption and reconstruction on the second feature matrix to obtain the watermark image. In this scheme, the cloud server acts as a relay station during image transmission, and the second device acquires the encrypted image from the cloud server, achieving secure data transmission. Furthermore, after acquiring the required image, decryption and watermark extraction are performed sequentially. Specifically, the embedded watermark undergoes inverse matrix factorization and decryption and reconstruction, maintaining the complete reconstruction capability of the watermark image, improving watermark extraction efficiency, and enhancing image robustness.

[0115] An image transmission method applied to a cloud server includes: first, receiving a first encrypted image sent by a first device; then, encrypting and converting the first encrypted image according to a first conversion key corresponding to the first device to obtain a second encrypted image; second, receiving a query request sent by a second device, the query request being used to request the acquisition of the encrypted image transmitted by the first device; finally, based on the query request, encrypting and converting the second encrypted image according to a second conversion key corresponding to the second device to obtain a third encrypted image; and sending the third encrypted image to the second device, wherein the second device can decrypt the third encrypted image using its second key. This method achieves secure image transmission based on a cloud server, realizing secure key conversion without disclosing the key to the cloud server, enabling the first device to encrypt using its own key and the second device to decrypt using its own second key. Attached Figure Description

[0116] Figure 1 This is a schematic flowchart of an image transmission method according to one embodiment of the present invention;

[0117] Figure 2 This is a schematic diagram illustrating the process of embedding a watermark image into a carrier image according to an embodiment of the present invention;

[0118] Figure 3 This is a schematic diagram of the process of performing multi-scale transformation and matrix decomposition on a carrier image according to an embodiment of the present invention;

[0119] Figure 4 This is a schematic diagram of the process of performing compressed sensing encryption and matrix decomposition on a watermarked image according to an embodiment of the present invention.

[0120] Figure 5 This is a schematic diagram illustrating the process of embedding a watermark image into a carrier image in the transform domain according to an embodiment of the present invention.

[0121] Figure 6 This is a schematic flowchart of an image transmission method according to another embodiment of the present invention;

[0122] Figure 7 This is a schematic diagram illustrating the process of extracting a watermark image from an encrypted image according to an embodiment of the present invention;

[0123] Figure 8 This is a schematic diagram of the process of performing multi-scale transformation and matrix decomposition on the target image according to an embodiment of the present invention;

[0124] Figure 9 This is a schematic diagram illustrating the process of restoring a watermarked image from the frequency domain to the spatial domain according to an embodiment of the present invention;

[0125] Figure 10This is a flowchart illustrating the gradient projection reconstruction algorithm according to an embodiment of the present invention;

[0126] Figure 11 This is a schematic flowchart of an image transmission method according to another embodiment of the present invention;

[0127] Figure 12 This is a schematic diagram of the process architecture of the image transmission method using an embodiment of the present invention;

[0128] Figure 13 In the figure, Figure (a) is a schematic diagram of the encrypted image according to an embodiment of the present invention, Figure (b) is a schematic diagram of the correctly decrypted image of Figure (a), and Figure (c) shows the image after changing the initial chaotic value. The following figure (a) is a schematic diagram of the decrypted image, and figure (d) shows the image with the initial chaotic value changed. Figure (a) shows the decrypted image, and Figure (e) shows the image after changing the initial chaotic value. The following figure (a) is a schematic diagram of the decrypted image;

[0129] Figure 14 This is a schematic diagram of pixel correlation in the horizontal, vertical and diagonal directions of the original image in an embodiment of the present invention;

[0130] Figure 15 This is a schematic diagram showing the pixel correlation of an encrypted image processed by the image transmission method of this invention in the horizontal, vertical, and diagonal directions.

[0131] Figure 16 This is a schematic diagram of the structure of the first network device according to an embodiment of the present invention;

[0132] Figure 17 This is a schematic diagram of the structure of the second network device according to an embodiment of the present invention;

[0133] Figure 18 This is a schematic diagram of the structure of the third network device according to an embodiment of the present invention;

[0134] Figure 19 This is a schematic diagram of the structure of an image transmission device according to one embodiment of the present invention;

[0135] Figure 20 This is a schematic diagram of the structure of an image transmission device according to another embodiment of the present invention;

[0136] Figure 21 This is a schematic diagram of the structure of an image transmission device according to another embodiment of the present invention. Detailed Implementation

[0137] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0138] The terms "first," "second," etc., used in this specification and claims are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0139] like Figure 1 As shown, an embodiment of the present invention provides an image transmission method applied to a first device, comprising:

[0140] Step S101: Perform multi-scale transformation and matrix decomposition on the carrier image to obtain the first feature matrix of the first region in the carrier image, wherein the first region is the region used to embed the watermark image.

[0141] Step S102: The watermark image is compressed, encrypted, and matrix decomposed to obtain the second feature matrix of the watermark image;

[0142] Step S103: Based on the first feature matrix, the second feature matrix, and the pre-configured embedding factor, the watermark image is embedded into the first region of the carrier image to obtain the target image;

[0143] Step S104: Perform compressed sensing encryption processing on the target image to generate an encrypted image;

[0144] Step S105: Send the encrypted image to the cloud server.

[0145] In this embodiment of the method, such as Figure 2As shown, the first device is the device corresponding to the image data owner. First, the carrier image undergoes multi-scale transformation and matrix decomposition to obtain a first feature matrix of a first region in the carrier image, where the first region is the area used to embed the watermark image. Then, the watermark image is compressed, encrypted, and decomposed to obtain a second feature matrix of the watermark image. Next, based on the first feature matrix, the second feature matrix, and a pre-configured embedding factor, the watermark image is embedded into the first region of the carrier image to obtain the target image. Finally, the target image undergoes compressed sensing encryption to generate an encrypted image, which is then sent to the cloud server. This method embeds the watermark into the image before transmission. Specifically, the watermark image to be embedded is compressed and encrypted, and the selected first region is embedded, effectively reducing the amount of embedded data while ensuring data security and improving watermark embedding efficiency. Furthermore, embedding the watermark image in different transform domains enhances the robustness of the watermark image against geometric attacks. During image transmission, the cloud server acts as a relay station, and the first device uploads the encrypted data to the cloud server, achieving secure data transmission.

[0146] In one embodiment, the function of sending the encrypted image to the cloud server in step S105 can be further explained. In this embodiment of the invention, each device has a key, and different devices have different keys. The key can be used to encrypt or decrypt images. If an image user (i.e., the second device) wants to use the target image generated by the first device, the second device cannot decrypt it. Therefore, a cloud server is set up. After receiving the encrypted image sent by the first device, the cloud server will use a conversion key to encrypt and convert the original first encrypted image transmitted by the first device into a third encrypted image that the second device can decrypt, which can effectively ensure the security of data transmission.

[0147] In one embodiment, optionally, the step of performing multi-scale transformation and matrix decomposition processing on the carrier image to obtain a first feature matrix of a first region in the carrier image includes:

[0148] Based on the dual-tree complex wavelet transform technique, the carrier image is processed by multi-scale transformation to obtain multiple frequency bands;

[0149] Based on the target frequency band among the multiple frequency bands, a first region in the carrier image is determined;

[0150] Based on the discrete cosine transform and singular value decomposition techniques, the first region is subjected to matrix decomposition to obtain the first feature matrix of the first region.

[0151] In this embodiment of the method, such as Figure 3As shown, firstly, the carrier image is subjected to Dual-Tree Complex Wavelet Transform (DTCWT) to decompose the carrier image into different frequency domains, obtaining multiple frequency bands. Specific operations include:

[0152] The carrier image is decomposed into L-level components using DTCWT, and the frequency band division has a deterministic structure, including:

[0153] Low-Low subband (LL): A low-frequency subband generated by the Lth level decomposition (largest scale, containing the main energy of the image).

[0154] The high-frequency subbands specifically include: Low-High subband (LH), High-Low subband (HL), and High-High subband (HH). Each level of decomposition produces 6 high-frequency subbands in 6 directions (±15°, ±45°, ±75°), for a total of 6×L high-frequency subbands.

[0155] It should be noted that DTCWT is an improved technique of discrete wavelet transform, which solves the problems of traditional wavelets and has the characteristics of ordinary wavelet decomposition, multi-directional resolution, approximate translation invariance, perfect reconstruction and data redundancy.

[0156] Then, select a target frequency band from the multiple frequency bands generated above and determine it as the first region in the carrier image;

[0157] Finally, based on the discrete cosine transform and singular value decomposition techniques, the first region is subjected to matrix decomposition to obtain the first feature matrix of the first region, which includes a diagonal matrix and an orthogonal matrix.

[0158] In one embodiment, optionally, determining the first region in the carrier image based on the target frequency band among the plurality of frequency bands includes:

[0159] Based on the characteristics of the human eye and the frequency of the frequency band, the frequency band is filtered, and the frequency band with the lowest frequency is determined as the target frequency band;

[0160] The region corresponding to the target frequency band is determined as the first region in the carrier image.

[0161] In this embodiment of the method, since the low-frequency sub-band has the largest scale and contains the main energy of the image, the lowest frequency band (i.e., the low-frequency sub-band) is selected as the target frequency band based on the characteristics of the human eye. The region corresponding to the target frequency band is determined as the first region in the carrier image for embedding the watermark image. The specific operations include:

[0162] By performing L-level decomposition on the carrier image using DTCWT, the low-frequency subband (LL) and high-frequency subband (LH, HL, HH) of each level are obtained. These multiple frequency bands are then sorted from low to high frequency to obtain:

[0163] LL (Level L) < LH (Level L) / HL (Level L) / HH (Level L) < LH (Level L-1) / HL (Level L-1) / HH (Level L-1) < ... < LH (Level 1) / HL (Level 1) / HH (Level 1);

[0164] Therefore, the lowest frequency band is clearly defined as LL (L level). The LL (L level) frequency band is determined as the target frequency band, and the area corresponding to the target frequency band is determined as the first area for embedding the watermark image.

[0165] In one embodiment, optionally, the step of performing matrix decomposition processing on the first region according to discrete cosine transform and singular value decomposition techniques to obtain the first feature matrix of the first region includes:

[0166] The first region is divided into blocks to obtain multiple first region blocks;

[0167] Perform discrete cosine transform on each of the first region blocks to obtain the frequency domain coefficient matrix;

[0168] Select the mid-frequency coefficients from the frequency domain coefficient matrix to construct the sparse matrix of the first region;

[0169] The sparse matrix is ​​subjected to singular value decomposition to obtain the first feature matrix of the first region.

[0170] In this embodiment of the method, firstly, the first region is divided into blocks to obtain multiple first region blocks. During the block division process, the computational complexity increases with the increase of the number of blocks, and larger blocks will reduce the block effect of the image. Therefore, in general, 8×8 or 16×16 blocks are used.

[0171] Then, a Discrete Cosine Transform (DCT) is performed on each first region block to obtain the frequency domain coefficient matrix of each first region block. The mid-frequency coefficients in the frequency domain coefficient matrix are then used to construct the sparse matrix of the first region, as shown in the following formula:

[0172]

[0173]

[0174] in, Indicates the image currently being processed. Value at position, This is the coefficient value located in the x-th row and y-th column after DCT processing, i.e., the sparse matrix. The size of the currently processed image is... H is the height of the image, and W is the width of the image. ,and .

[0175] It should be noted that DCT is a symmetric and separable orthogonal transformation, and it possesses strong concentrated energy characteristics and good decorrelation properties, ensuring the quality of the embedded watermark. If an image is directly processed using DCT without prior processing, the algorithm complexity will be high. Dividing the image into blocks before DCT processing effectively reduces the computational complexity of the DCT algorithm.

[0176] Finally, singular value decomposition is performed on the sparse matrix to obtain the first characteristic matrix of the first region. The first characteristic matrix includes a diagonal matrix and an orthogonal matrix.

[0177] In one embodiment, optionally, the step of compressing, encrypting, and decomposing the watermark image to obtain a second feature matrix of the watermark image includes:

[0178] Based on the first key, the watermark image is sequentially subjected to compressed sensing sampling and chaotic encryption processing to obtain an encrypted watermark image, wherein the first key is randomly generated based on a chaotic sequence;

[0179] The encrypted watermark image is subjected to matrix decomposition processing based on discrete cosine transform and singular value decomposition techniques to obtain the second feature matrix of the encrypted watermark image.

[0180] In this embodiment of the method, such as Figure 4 As shown, step S102 is further explained as follows: First, based on the first key, the watermark image is sequentially subjected to compressed sensing sampling and chaotic encryption processing to obtain an encrypted watermark image. The first key is randomly generated based on a chaotic sequence and includes the user sampling key. and encryption scrambling key The specific steps are as follows:

[0181] First, using the measurement matrix (i.e., the user sampling key). Compressed sensing sampling is performed on the watermark image, such as... Figure 4 As shown, Figure 4 middle Represents the pixel matrix of the watermarked image. This represents one of the sparse bases. This indicates that the watermarked image is on a sparse basis. Sparse representation of the following, This represents the compressed and encrypted watermark image. This invention achieves a dual breakthrough in compression and reversibility through sparse sampling, contrasting with the irreversibility of traditional hash compression. This effectively reduces the amount of embedded data while maintaining the ability to reconstruct the complete watermark information.

[0182] Furthermore, it should be noted that the size of the above measurement matrix depends on the image size. For example, for a grayscale image with a size of 256*256, the measurement matrix size can be 128*256, in which case 128*256 chaotic random values ​​are arranged to form the measurement matrix.

[0183] Furthermore, by utilizing a measurement matrix of extremely small dimensions Generate a Message Authentication Code (MAC) for the watermark image, and then combine the MAC with the compressed and encrypted watermark image. The images are stitched together to obtain the first image. The MAC (Message Access Detection) is a short piece of information generated using a specific algorithm, used to check the integrity of a message and for authentication. Specifically, the MAC can be used to check whether the content of a message has been altered during transmission, regardless of whether the alteration was accidental or due to a deliberate attack. It can also be used to authenticate the message's source, confirming its origin.

[0184] Then, using the encrypted scrambling key The first image after stitching is subjected to chaotic encryption processing to obtain an encrypted watermark image, as shown in the following formula:

[0185]

[0186] in, This indicates an encrypted watermarked image. This represents the first image after stitching. This represents the encryption / scrambling key.

[0187] Finally, to embed the encrypted watermark image into the diagonal matrix of the first feature matrix, the encrypted watermark image needs to be segmented, processed by DCT, and decomposed by singular values. Specifically, first, the encrypted watermark image is segmented into multiple watermark image blocks. Then, DCT is performed on each watermark image block, and a sparse matrix is ​​constructed based on the intermediate frequency coefficients obtained from the DCT. Finally, singular value decomposition is performed on the sparse matrix to obtain the second feature matrix, which includes a diagonal matrix and an orthogonal matrix. It should be noted that the above steps are the same as the steps for segmenting, processing by DCT, and decomposing the first region, and will not be repeated here.

[0188] In one embodiment, optionally, the step of embedding the watermark image into the first region of the carrier image based on the first feature matrix, the second feature matrix, and a pre-configured embedding factor to obtain the target image includes:

[0189] The third feature matrix is ​​obtained by calculating the second feature matrix into the first feature matrix based on the first feature matrix, the second feature matrix, and the pre-configured embedding factor.

[0190] The third feature matrix is ​​subjected to inverse matrix decomposition and multi-scale inverse transformation to obtain the target region, wherein the target region is the first region embedded in the watermark image;

[0191] The carrier image is reconstructed based on the target region to obtain the target image.

[0192] In this embodiment of the method, such as Figure 5 As shown, step S103 is further explained as follows: First, by using a pre-set embedding factor k, the diagonal matrix in the second feature matrix is ​​embedded into the diagonal matrix in the first feature matrix to generate the third feature matrix. By embedding watermark image information in different transform domains, the robustness of the watermark image to geometric attacks is enhanced. The embedding formula is as follows:

[0193]

[0194] in, This represents the diagonal matrix in the first feature matrix corresponding to the first region of the carrier image. This represents the diagonal matrix in the second feature matrix corresponding to the watermarked image. Represents the third characteristic matrix. This represents the embedding factor, which controls the mixing ratio between the watermark energy and the host image.

[0195] Then, the third feature matrix is ​​processed sequentially with inverse singular value, inverse DCT, and inverse DTCWT to reconstruct the low-frequency subband and the image, respectively, to obtain the image of the first region. The inverse DCT process uses the orthogonal matrix in the first feature matrix and the orthogonal matrix in the second feature matrix. It should be noted that the inverse singular value, inverse DCT, and inverse DTCWT processes are all the inverse processes of the above singular value, DCT, and DTCWT processes, which will not be elaborated here.

[0196] Finally, the image of the target region is stitched together with the original carrier image to reconstruct the target image with the embedded watermark. Verification shows that the peak signal-to-noise ratio (PSNR) of the target image obtained in this embodiment of the invention is greater than or equal to 30 dB. PSNR is an important factor for evaluating the impact of watermark embedding on the quality of the carrier image.

[0197] In one embodiment, optionally, performing compressed sensing encryption processing on the target image to generate an encrypted image includes:

[0198] The target image is compressed sensing encryption using a first key to generate an encrypted image, wherein the first key is randomly generated based on a chaotic sequence.

[0199] In this embodiment of the method, the first key is randomly generated based on a chaotic sequence, and the first key includes the user sampling key. and encryption scrambling key The target image is compressed sensing encryption using the first key to generate an encrypted image, as shown in the following formula:

[0200]

[0201] in, This represents the user sampling key corresponding to the first device. This represents the encryption / scrambling key corresponding to the first device. Represents the target image. This indicates an encrypted image.

[0202] like Figure 6 As shown, this embodiment of the invention also provides an image transmission method applied to a second device, comprising:

[0203] Step S601: Obtain the encrypted image sent by the cloud server, wherein the encrypted image is an encrypted target image and the target image embeds an encrypted watermark image;

[0204] Step S602: Decrypt and reconstruct the encrypted image to obtain the target image;

[0205] Step S603: Perform multi-scale transformation and matrix decomposition on the target image to obtain the third feature matrix of the target image;

[0206] Step S604: Extract the encrypted watermark image from the third feature matrix according to the pre-configured embedding factor to obtain the second feature matrix corresponding to the watermark image;

[0207] Step S605: Perform matrix inverse decomposition and decryption reconstruction on the second feature matrix to obtain the watermark image.

[0208] In this embodiment of the method, such as Figure 7 As shown, the second device is the device corresponding to the user of the image data. First, it acquires the encrypted image sent by the cloud server, where the encrypted image is the encrypted target image, and the target image embeds an encrypted watermark image. Then, it decrypts and reconstructs the encrypted image to obtain the target image. Next, it performs multi-scale transformation and matrix factorization on the target image to obtain the third feature matrix of the target image. Next, according to a pre-configured embedding factor, it extracts the encrypted watermark image from the third feature matrix to obtain the second feature matrix corresponding to the watermark image. Finally, it performs inverse matrix factorization and decryption and reconstruction on the second feature matrix to obtain the watermark image. In this method, the cloud server acts as a relay station during image transmission, and the second device acquires the encrypted image from the cloud server, achieving secure data transmission. Furthermore, after acquiring the required image, it sequentially performs decryption and watermark extraction in different transform domains. Specifically, it performs inverse matrix factorization and decryption and reconstruction on the embedded watermark, achieving complete reconstruction of the watermark image and improving watermark extraction efficiency. In addition, because the watermark image is embedded in different transform domains, it enhances the robustness of the watermark image against geometric attacks.

[0209] Optionally, in one embodiment, the method further includes:

[0210] Send a query request to the cloud server, the query request being used to request the acquisition of the encrypted image transmitted by the first device;

[0211] The step of obtaining the encrypted image sent by the cloud server includes:

[0212] Receive the encrypted image sent by the cloud server in accordance with the query request.

[0213] In this embodiment of the method, before step S601, the second device actively sends a query request to the cloud server to request the encrypted image transmitted by the first device. It should be noted that in this embodiment, each device has a key, and different devices use different keys. This key can be used to encrypt or decrypt images. Therefore, the second device cannot directly decrypt the encrypted image generated by the first device and needs to send a query request to the cloud server. The cloud server then uses a conversion key to encrypt and convert the original first encrypted image transmitted by the first device into a third encrypted image that the second device can decrypt, effectively ensuring the security of data transmission. Therefore, in this embodiment, the image sent by the cloud server to the second device is the converted third encrypted image.

[0214] In one embodiment, optionally, the step of decrypting and reconstructing the encrypted image to obtain the target image includes:

[0215] The encrypted image is decrypted and reconstructed using a second key to obtain the target image. The second key is randomly generated based on a chaotic sequence.

[0216] In this embodiment of the method, the second key is randomly generated based on a chaotic sequence, and the second key includes the user sampling key. and encryption scrambling key The encrypted image is decrypted and reconstructed using the second key to obtain the target image. First, the encrypted image is scrambled and restored using the following formula:

[0217]

[0218] in, This represents the encryption / scrambling key corresponding to the first device. This indicates an encrypted image sent by the cloud server. This represents the image after initial decryption.

[0219] Then, using the user sampling key The image after initial decryption Image reconstruction is performed, specifically including: using the user sampling key As input parameters, greedy iteration or convex optimization algorithms are used to... Reconstruct the target image. .

[0220] In one embodiment, optionally, the step of performing multi-scale transformation and matrix decomposition on the target image to obtain the third feature matrix of the target image includes:

[0221] The target image is subjected to multi-scale transformation processing based on the dual-tree complex wavelet transform technique to obtain multiple frequency bands;

[0222] Based on the target frequency band among the multiple frequency bands, a target region in the target image is determined, wherein the target region is the region embedded in the watermark image;

[0223] The target region is subjected to matrix decomposition based on discrete cosine transform and singular value decomposition techniques to obtain the third characteristic matrix of the target region.

[0224] In this embodiment of the method, such as Figure 8 As shown, firstly, the target image is processed by DTCWT to decompose it into different frequency domains, obtaining multiple frequency bands. Specific operations include:

[0225] The target image is decomposed into L-level components using DTCWT, and the frequency band division has a deterministic structure, including:

[0226] Low-Low subband (LL): A low-frequency subband generated by the Lth level decomposition (largest scale, containing the main energy of the image).

[0227] The high-frequency subbands specifically include: Low-High subband (LH), High-Low subband (HL), and High-High subband (HH). Each level of decomposition produces 6 high-frequency subbands in 6 directions (±15°, ±45°, ±75°), for a total of 6×L high-frequency subbands.

[0228] It should be noted that DTCWT is an improved technique of discrete wavelet transform, which solves the problems of traditional wavelets and has the characteristics of ordinary wavelet decomposition, multi-directional resolution, approximate translation invariance, perfect reconstruction and data redundancy.

[0229] Then, select the target region for embedding the watermark image from the multiple frequency bands generated above.

[0230] Finally, based on the discrete cosine transform and singular value decomposition techniques, the target region is subjected to matrix decomposition to obtain the third characteristic matrix of the target region, which includes a diagonal matrix and an orthogonal matrix.

[0231] In one embodiment, optionally, determining the target region in the target image based on the target frequency band among the plurality of frequency bands includes:

[0232] Based on the characteristics of the human eye and the frequency of the frequency band, the frequency band is filtered, and the frequency band with the lowest frequency is determined as the target frequency band;

[0233] The region corresponding to the target frequency band is determined as the target region in the target image.

[0234] In this embodiment of the method, since the low-frequency sub-band has the largest scale and contains the main energy of the image, when embedding the watermark image, this embodiment embeds the watermark image into the region corresponding to the low-frequency sub-band. Similarly, when extracting the watermark image, the frequency band with the lowest frequency (i.e., the low-frequency sub-band) is the target frequency band for embedding the watermark image. The specific operation is as follows:

[0235] Based on the characteristics of the human eye and the frequency of the frequency band, the frequency band is filtered. After performing L-level decomposition of the carrier image using DTCWT, the low-frequency sub-band (LL) and high-frequency sub-band (LH, HL, HH) of each level are obtained. The multiple frequency bands are then sorted from low to high frequency to obtain:

[0236] LL (Level L) < LH (Level L) / HL (Level L) / HH (Level L) < LH (Level L-1) / HL (Level L-1) / HH (Level L-1) < ... < LH (Level 1) / HL (Level 1) / HH (Level 1);

[0237] Therefore, the lowest frequency band is clearly LL (L level). The LL (L level) frequency band is determined as the target frequency band, and the area corresponding to the target frequency band is determined as the target area in the target image for embedding the watermark image.

[0238] In one embodiment, optionally, the step of performing matrix decomposition processing on the target region according to discrete cosine transform and singular value decomposition techniques to obtain the third feature matrix of the target region includes:

[0239] The target region is divided into blocks to obtain multiple target region blocks;

[0240] Perform discrete cosine transform on each of the target regions to obtain the frequency domain coefficient matrix;

[0241] Select the mid-frequency coefficients from the frequency domain coefficient matrix to construct the sparse matrix of the target region;

[0242] The sparse matrix is ​​subjected to singular value decomposition to obtain the third feature matrix of the target region.

[0243] In this embodiment of the method, the target region is first divided into blocks to obtain multiple target region blocks. During the block division process, the computational complexity increases with the number of blocks, and larger blocks reduce the block effect of the image. Therefore, in general, 8×8 or 16×16 blocks are used.

[0244] Then, DCT processing is performed on each target region block to obtain the frequency domain coefficient matrix of each target region block. The intermediate frequency coefficients in the frequency domain coefficient matrix are then used to construct the sparse matrix of the target region, as shown in the following formula:

[0245]

[0246]

[0247] in, Indicates the image currently being processed. Value at position, This is the coefficient value located in the x-th row and y-th column after DCT processing, i.e., the sparse matrix. The size of the currently processed image is... H is the height of the image, and W is the width of the image. ,and .

[0248] It should be noted that DCT is a symmetric and separable orthogonal transformation, and it possesses strong concentrated energy characteristics and good decorrelation properties, ensuring the quality of the embedded watermark. If an image is directly processed using DCT without prior processing, the algorithm complexity will be high. Dividing the image into blocks before DCT processing effectively reduces the computational complexity of the DCT algorithm.

[0249] Finally, singular value decomposition is performed on the sparse matrix to obtain the third characteristic matrix of the target region, which includes a diagonal matrix and an orthogonal matrix.

[0250] In one embodiment, optionally, the method for extracting the encrypted watermark image from the third feature matrix according to a pre-configured embedding factor, and obtaining the second feature matrix corresponding to the watermark image, includes:

[0251] The encrypted watermark image is extracted from the third feature matrix using a pre-set embedding factor k, and a second feature matrix is ​​generated. The extraction formula is as follows:

[0252]

[0253] in, This represents the diagonal matrix of the carrier image used to embed the watermark image in the target image. This represents the diagonal matrix in the second feature matrix corresponding to the watermarked image. This represents the diagonal matrix in the third characteristic matrix. This represents the embedding factor, which controls the mixing ratio between the watermark energy and the host image.

[0254] In one embodiment, optionally, the step of performing matrix inverse decomposition and decryption reconstruction on the second feature matrix to obtain the watermark image includes:

[0255] Perform matrix inverse decomposition on the second feature matrix to obtain the encrypted watermark image;

[0256] Based on the second key, the encrypted watermark image is sequentially subjected to chaotic decryption and compressed sensing recovery processing to obtain the watermark image, wherein the second key is randomly generated based on a chaotic sequence.

[0257] In this embodiment of the method, such as Figure 9As shown, the second feature matrix is ​​sequentially processed by inverse singular value and inverse DCT to restore the watermark image from the frequency domain to the spatial domain, thereby obtaining the encrypted watermark image. The inverse DCT process uses the orthogonal matrix in the second feature matrix. It should be noted that the inverse singular value, inverse DCT, and inverse DTCWT processes are all the inverse processes of the above singular value and DCT processes, and will not be elaborated here.

[0258] Finally, based on the second key, the encrypted watermark image is sequentially subjected to chaotic decryption and compressed sensing restoration processing. The second key is randomly generated based on a chaotic sequence and includes the user sampling key. and encryption scrambling key The specific operations include:

[0259] First, the encrypted watermark image is scrambled and restored using the following formula:

[0260]

[0261] in, This represents the encryption / scrambling key corresponding to the first device. This represents the encrypted watermark image. This represents the watermark image after initial decryption. It's important to note that this is the watermark image after decryption at this stage. It's still a compressed image.

[0262] Then, using the user sampling key The image after initial decryption Image reconstruction is performed, specifically including: using the user sampling key As input parameters, the gradient projection reconstruction algorithm is used to... Reconstruct the image to obtain the watermark image. .like Figure 10 As shown, the gradient projection reconstruction algorithm is an iterative method for constrained optimization problems. It solves for the optimal solution by projecting the gradient onto the feasible region. Its core idea is to project the updated points into the feasible region in each iteration, ensuring that the solution always satisfies the constraints. Under these constraints, a watermarked image is obtained.

[0263] Furthermore, by performing chaotic decryption on the encrypted watermark image, one obtains... After that, it is also possible to calculate The correlation coefficient is normalized to obtain the bit error rate (BER) value, which is used to verify the transmission reliability, anti-interference ability and transmission efficiency of the image transmission method provided in this embodiment of the invention.

[0264] like Figure 11As shown, this embodiment of the invention also provides an image transmission method applied to a cloud server, comprising:

[0265] Step S1101: Receive the first encrypted image sent by the first device;

[0266] Step S1102: Based on the first conversion key corresponding to the first device, the first encrypted image is encrypted and converted to obtain the second encrypted image;

[0267] Step S1103: Receive a query request sent by the second device, the query request being used to request the acquisition of the encrypted image transmitted by the first device;

[0268] Step S1104: Based on the query request, and according to the second conversion key corresponding to the second device, the second encrypted image is encrypted and converted to obtain a third encrypted image;

[0269] Step S1105: Send the third encrypted image to the second device, wherein the second device is able to decrypt the third encrypted image using the second key of the second device.

[0270] In this embodiment of the method, each device has a key, and different devices have different keys. This key can be used to encrypt or decrypt images. If a second device wants to use the target image generated by the first device, the second device cannot directly decrypt the image encrypted by the first device. Therefore, a cloud server is set up. After receiving the encrypted image sent by the first device, the cloud server will use a conversion key to encrypt and convert the original first encrypted image transmitted by the first device into a third encrypted image that the second device can decrypt. This can effectively ensure the security of data transmission and achieve secure key conversion without disclosing the key to the cloud server. This allows the first device to use its own key to complete the encryption, and the second device to use its own second key to complete the decryption.

[0271] Optionally, in one embodiment, the method further includes:

[0272] Obtain a first conversion key, wherein the first conversion key includes a first matrix, the first matrix is ​​randomly generated according to a chaotic sequence, and the first matrix is ​​used to generate a first key, the first key is used to encrypt and generate the first encrypted image;

[0273] Obtain a second conversion key, wherein the second conversion key includes a second matrix, the second matrix is ​​randomly generated according to a chaotic sequence, and the second matrix is ​​used to generate a second key, the second key being used to decrypt the third encrypted image.

[0274] In this embodiment of the method, the key center randomly generates a key for each device based on the chaotic sequence, and also generates a conversion key for each device. It then sends the conversion keys for all devices to the cloud server. The cloud server uses the conversion keys to operate on the received encrypted image. The specific method includes:

[0275] For example: The first key of the first device includes: the user sampling key and encryption scrambling key The specific generation method includes: first, randomly generating a compressed matrix using a chaotic sequence. A scrambling matrix Then for the first device Randomly generate orthogonal matrices ,calculate Randomly generate invertible matrices ,calculate .

[0276] The first conversion key corresponding to the first device includes: and In step S1102, after the cloud server receives the first encrypted image sent by the first device, it converts the first encrypted image using the first conversion key, as shown in the following formula:

[0277]

[0278] in, This indicates the first encrypted image sent by the first device. This represents the second encrypted image after conversion by the cloud server. Represents the orthogonal matrix in the first transformation key The inverse matrix, Represents the invertible matrix in the first transformation key. The inverse matrix, Represents a compression matrix. Represents the scrambling matrix. This represents the unencrypted original image corresponding to the first encrypted image.

[0279] The second key for the second device includes: the user sampling key. and encryption scrambling key The specific generation method includes: first, randomly generating a compressed matrix using a chaotic sequence. A scrambling matrix Then for the second device Randomly generate orthogonal matrices ,calculate Randomly generate invertible matrices ,calculate .

[0280] The second conversion key corresponding to the second device includes: and In step S1104, after receiving the query request sent by the second device, the cloud server uses the second conversion key to convert the second encrypted image, as shown in the following formula:

[0281]

[0282] in, This refers to the third encrypted image after the second conversion by the cloud server, which is the third encrypted image sent by the cloud server to the second device. This represents the second encrypted image after the first conversion by the cloud server. Represents the orthogonal matrix in the second transformation key The inverse matrix, Represents the invertible matrix in the second transformation key. The inverse matrix, Represents a compression matrix. Represents the scrambling matrix. This represents the unencrypted original image corresponding to the first encrypted image.

[0283] In step S1105, the cloud server sends a third encrypted image to the second device. The second device can encrypt the third image using the second key. Decryption and reconstruction are performed to obtain the unencrypted original image. .

[0284] like Figure 12 As shown in the figure, this embodiment of the invention also provides a schematic diagram of the image transmission process between a key center, a first device, a second device, and a cloud server. The specific steps of the image transmission are as follows:

[0285] In step S1201, the key center generates a first key and a first conversion key corresponding to the first device, a second key and a second conversion key corresponding to the second device, and sends the first key to the first device, the second key to the second device, and the first conversion key and the second conversion key to the cloud server.

[0286] In step S1202, the first device executes steps S101-S104 according to the first key. In simple terms, firstly, the watermark image is compressed and encrypted using the first key and embedded into the carrier image to generate the target image. Then, the target image is encrypted using the first key to generate the first encrypted image.

[0287] Step S1203: The first device sends the first encrypted image to the cloud server;

[0288] In step S1204, the cloud server converts the first encrypted image using the first conversion key to generate a second encrypted image;

[0289] Step S1205: The second device sends a query request to the cloud server to obtain the encrypted image sent by the first device;

[0290] In step S1206, after receiving the query request, the cloud server converts the second encrypted image using the second conversion key to generate a third encrypted image and sends the third encrypted image to the second device.

[0291] In step S1207, the second device performs steps S602-S605 on the third encrypted image according to the second key. In simple terms, firstly, the third encrypted image is decrypted and reconstructed using the second key to obtain the target image. Then, the watermark image is extracted from the target image and decrypted and reconstructed.

[0292] Furthermore, the method for generating the first key and first conversion key corresponding to the first device, and the second key and second conversion key corresponding to the second device in step S1201 is described. This embodiment of the invention provides a method for generating random chaotic sequences and keys based on a three-dimensional chaotic mapping using a one-dimensional sine map and a Chebyshev map, as detailed below:

[0293] A three-dimensional chaotic system is a nonlinear dynamical system in a three-dimensional state space. Its trajectory forms a chaotic attractor in phase space, which manifests as follows:

[0294] Initial value sensitivity: Small differences in initial values ​​can cause the orbit to diverge exponentially.

[0295] Topological mixing: The phase space orbitals are dense and cannot be decomposed.

[0296] Non-periodic: No fixed period or quasi-periodic solution.

[0297] A three-dimensional chaotic system is usually represented as:

[0298]

[0299] The key center generates a chaotic sequence of appropriate length based on a three-dimensional chaotic system, as follows:

[0300]

[0301] in, , and They are three different chaotic random sequences. , This represents a fixed parameter in a chaotic system.

[0302] according to The sequence is rearranged into a matrix of a specific size, and a compressed matrix is ​​randomly generated from the chaotic sequence. A scrambling matrix .

[0303] Then for the first device Randomly generate orthogonal matrices ,calculate Randomly generate invertible matrices ,calculate Therefore, the first key includes and The first conversion key includes and ;

[0304] For the second device Randomly generate orthogonal matrices ,calculate Randomly generate invertible matrices ,calculate Therefore, the second key includes and The first conversion key includes and .

[0305] Furthermore, it should be noted that the image transmission method provided in this invention has sufficient security and attack resistance compared to existing technologies. The security will be discussed below from two aspects: key space and sensitivity.

[0306] 1) The keys generated in this embodiment of the invention have a huge key space. Specifically, two three-dimensional chaotic sequences participate in the generation of two matrices. This involves chaotic parameters. , , , For a 64-bit central processing unit, providing 16 bits of precision for double-precision floating-point numbers, the key space is approximately... .

[0307] 2) Key sensitivity refers to the ability of a tiny perturbation to the key to result in entirely different ciphertext images and decryption results. In other words, for a well-designed image encryption system to be effective, it should be highly sensitive to changes in different keys. The initial condition sensitivity of chaotic systems is one of their most prominent characteristics. Even a minute change in the initial input of a chaotic sequence can lead to significant differences between the original and the new sequence. For example... Figure 13As shown, Figure (a) is the encrypted image, Figure (b) is the correctly decrypted image, and Figure (c) shows the image after changing the initial chaotic value. The decrypted image, Figure (d) shows the chaotic initial value after changing. The decrypted image, Figure (e) shows the chaotic initial value after changing. The decryption images show that, during the key generation process of this method, no matter how small the change is made to the initial input of the chaotic sequence involved, the decryption result is completely different from the decryption result generated using incorrect input. In fact, after modifying the initial value of the chaotic sequence, the reconstruction result is almost impossible to identify, even if the modification is very small.

[0308] The following discussion focuses on the correlation between adjacent pixels to assess attack resistance:

[0309] Typically, adjacent pixel pairs in natural images exhibit strong correlations in the horizontal, vertical, or diagonal directions. Obviously, attackers might exploit this characteristic to obtain usable information based on information they already know. Therefore, the ability to reduce the strong correlation between adjacent pixels becomes one of the indicators of the quality of an image cryptography system. Numerically, this ability can be demonstrated by reducing the correlation value of adjacent pixels to near 0 in the horizontal, vertical, and diagonal directions. In fact, when the correlation value of adjacent pixels is in the range of [−0.3, 0.3], adjacent pixels in an image can be considered almost independent. That is, after processing with compressed sensing methods, the strong correlation between adjacent pixel pairs in the original image is broken. The method for calculating image pixel correlation is as follows; taking the correlation of horizontal adjacent pixels as an example, the correlation coefficient can be calculated as:

[0310]

[0311]

[0312]

[0313]

[0314]

[0315]

[0316] in,( , () represents a pair of horizontally adjacent pixels. Represents the left pixel in a pair of N adjacent pixels. The expected mean, Represents the right pixel in a pair of N adjacent pixels. The expected mean, and Let x and y represent the variances, respectively. Represents the slope of x and y. This represents the correlation coefficient between x and y.

[0317] Using the above method, the pixel correlation of the original image and the encrypted image processed by the method provided in this embodiment of the invention is calculated in the horizontal, vertical, and diagonal directions, respectively. Figure 14 and Figure 15 As shown, Figure 14 The pixel correlation of the original image in the horizontal, vertical, and diagonal directions. Figure 15 To analyze the pixel correlation of the encrypted image processed by the method provided in this embodiment of the invention in the horizontal, vertical and diagonal directions, it can be seen that the original image has strong correlation. Information about the other pixel in the same pixel pair can be extracted and analyzed through the information of one pixel. The encrypted image has almost no correlation, which can effectively prevent attacks.

[0318] like Figure 16 As shown, this embodiment of the invention also provides a first network device 1600, including a transceiver 1610 and a processor 1620, wherein,

[0319] The processor 1620 is used for:

[0320] The carrier image is subjected to multi-scale transformation and matrix decomposition to obtain the first feature matrix of the first region in the carrier image, wherein the first region is the region used to embed the watermark image.

[0321] The watermark image is compressed, encrypted, and matrix decomposed to obtain the second feature matrix of the watermark image;

[0322] Based on the first feature matrix, the second feature matrix, and the pre-configured embedding factor, the watermark image is embedded into the first region of the carrier image to obtain the target image;

[0323] The target image is subjected to compressed sensing encryption processing to generate an encrypted image;

[0324] The transceiver 1610 is used for:

[0325] The encrypted image is sent to the cloud server.

[0326] Optionally, the processor 1620 is further configured to:

[0327] Based on the dual-tree complex wavelet transform technique, the carrier image is processed by multi-scale transformation to obtain multiple frequency bands;

[0328] Based on the target frequency band among the multiple frequency bands, a first region in the carrier image is determined;

[0329] Based on the discrete cosine transform and singular value decomposition techniques, the first region is subjected to matrix decomposition to obtain the first feature matrix of the first region.

[0330] Optionally, the processor 1620 is further configured to:

[0331] Based on the characteristics of the human eye and the frequency of the frequency band, the frequency band is filtered, and the frequency band with the lowest frequency is determined as the target frequency band;

[0332] The region corresponding to the target frequency band is determined as the first region in the carrier image.

[0333] Optionally, the processor 1620 is further configured to:

[0334] The first region is divided into blocks to obtain multiple first region blocks;

[0335] Perform discrete cosine transform on each of the first region blocks to obtain the frequency domain coefficient matrix;

[0336] Select the mid-frequency coefficients from the frequency domain coefficient matrix to construct the sparse matrix of the first region;

[0337] The sparse matrix is ​​subjected to singular value decomposition to obtain the first feature matrix of the first region.

[0338] Optionally, the processor 1620 is further configured to:

[0339] Based on the first key, the watermark image is sequentially subjected to compressed sensing sampling and chaotic encryption processing to obtain an encrypted watermark image, wherein the first key is randomly generated based on a chaotic sequence;

[0340] The encrypted watermark image is subjected to matrix decomposition processing based on discrete cosine transform and singular value decomposition techniques to obtain the second feature matrix of the encrypted watermark image.

[0341] Optionally, the processor 1620 is further configured to:

[0342] The third feature matrix is ​​obtained by calculating the second feature matrix into the first feature matrix based on the first feature matrix, the second feature matrix, and the pre-configured embedding factor.

[0343] The third feature matrix is ​​subjected to inverse matrix decomposition and multi-scale inverse transformation to obtain the target region, wherein the target region is the first region embedded in the watermark image;

[0344] The carrier image is reconstructed based on the target region to obtain the target image.

[0345] Optionally, the processor 1620 is further configured to:

[0346] The target image is compressed sensing encryption using a first key to generate an encrypted image, wherein the first key is randomly generated based on a chaotic sequence.

[0347] like Figure 17 As shown, this embodiment of the invention also provides a second network device 1700, including a transceiver 1710 and a processor 1720, wherein,

[0348] The transceiver 1710 is used for:

[0349] Obtain an encrypted image sent by a cloud server, wherein the encrypted image is an encrypted target image, and the target image embeds an encrypted watermark image;

[0350] The processor 1720 is used for:

[0351] The encrypted image is decrypted and reconstructed to obtain the target image;

[0352] The target image is subjected to multi-scale transformation and matrix decomposition to obtain the third feature matrix of the target image;

[0353] Based on the pre-configured embedding factor, the encrypted watermark image is extracted from the third feature matrix to obtain the second feature matrix corresponding to the watermark image;

[0354] The second feature matrix is ​​subjected to matrix inverse decomposition and decryption reconstruction to obtain the watermark image.

[0355] Optionally, the transceiver 1710 is further configured to:

[0356] Send a query request to the cloud server, the query request being used to request the acquisition of the encrypted image transmitted by the first device;

[0357] The step of obtaining the encrypted image sent by the cloud server includes:

[0358] Receive the encrypted image sent by the cloud server in accordance with the query request.

[0359] Optionally, the processor 1720 is further configured to:

[0360] The encrypted image is decrypted and reconstructed using a second key to obtain the target image. The second key is randomly generated based on a chaotic sequence.

[0361] Optionally, the processor 1720 is further configured to:

[0362] The target image is subjected to multi-scale transformation processing based on the dual-tree complex wavelet transform technique to obtain multiple frequency bands;

[0363] Based on the target frequency band among the multiple frequency bands, a target region in the target image is determined, wherein the target region is the region embedded in the watermark image;

[0364] The target region is subjected to matrix decomposition based on discrete cosine transform and singular value decomposition techniques to obtain the third characteristic matrix of the target region.

[0365] Optionally, the processor 1720 is further configured to:

[0366] Based on the characteristics of the human eye and the frequency of the frequency band, the frequency band is filtered, and the frequency band with the lowest frequency is determined as the target frequency band;

[0367] The region corresponding to the target frequency band is determined as the target region in the target image.

[0368] Optionally, the processor 1720 is further configured to:

[0369] The target region is divided into blocks to obtain multiple target region blocks;

[0370] Perform discrete cosine transform on each of the target regions to obtain the frequency domain coefficient matrix;

[0371] Select the mid-frequency coefficients from the frequency domain coefficient matrix to construct the sparse matrix of the target region;

[0372] The sparse matrix is ​​subjected to singular value decomposition to obtain the third feature matrix of the target region.

[0373] Optionally, the processor 1720 is further configured to:

[0374] Perform matrix inverse decomposition on the second feature matrix to obtain the encrypted watermark image;

[0375] Based on the second key, the encrypted watermark image is sequentially subjected to chaotic decryption and compressed sensing recovery processing to obtain the watermark image, wherein the second key is randomly generated based on a chaotic sequence.

[0376] like Figure 18 As shown, this embodiment of the invention also provides a third network device 1800, including a transceiver 1810 and a processor 1820, wherein,

[0377] The transceiver 1810 is used to receive the first encrypted image sent by the first device;

[0378] The processor 1820 is used to perform encryption conversion on the first encrypted image according to the first conversion key corresponding to the first device to obtain a second encrypted image;

[0379] The transceiver 1810 is also used to receive a query request sent by the second device, the query request being used to request the acquisition of an encrypted image transmitted by the first device;

[0380] The processor 1820 is further configured to, based on the query request and according to the second conversion key corresponding to the second device, perform encryption conversion on the second encrypted image to obtain a third encrypted image;

[0381] The transceiver 1810 is also used to send the third encrypted image to the second device, wherein the second device is capable of decrypting the third encrypted image using the second key of the second device.

[0382] Optionally, the transceiver 1810 is further configured to:

[0383] Obtain a first conversion key, wherein the first conversion key includes a first matrix, the first matrix is ​​randomly generated according to a chaotic sequence, and the first matrix is ​​used to generate a first key, the first key is used to encrypt and generate the first encrypted image;

[0384] Obtain a second conversion key, wherein the second conversion key includes a second matrix, the second matrix is ​​randomly generated according to a chaotic sequence, and the second matrix is ​​used to generate the second key, the second key being used to decrypt the third encrypted image.

[0385] like Figure 19 As shown, this embodiment of the invention also provides an image transmission device, applied to a first device, comprising:

[0386] The first processing module 1901 is used to perform multi-scale transformation and matrix decomposition processing on the carrier image to obtain the first feature matrix of the first region in the carrier image, wherein the first region is the region used to embed the watermark image.

[0387] The second processing module 1902 is used to compress, encrypt, and decompose the watermark image to obtain the second feature matrix of the watermark image.

[0388] The first embedding module 1903 is used to embed the watermark image into the first region of the carrier image according to the first feature matrix, the second feature matrix and the pre-configured embedding factor to obtain the target image;

[0389] The first encryption module 1904 is used to perform compressed sensing encryption processing on the target image to generate an encrypted image;

[0390] The first sending module 1905 is used to send the encrypted image to the cloud server.

[0391] Optionally, the first processing module 1901 includes:

[0392] The first processing submodule is used to perform multi-scale transformation processing on the carrier image according to the dual-tree complex wavelet transform technique to obtain multiple frequency bands;

[0393] The first determining submodule is used to determine a first region in the carrier image based on the target frequency band among the multiple frequency bands;

[0394] The second processing submodule is used to perform matrix decomposition processing on the first region according to the discrete cosine transform and singular value decomposition techniques to obtain the first feature matrix of the first region.

[0395] Optionally, the first determining submodule includes:

[0396] The first screening unit is used to screen the frequency band according to the characteristics of the human eye and the frequency of the frequency band, and to determine the frequency band with the lowest frequency as the target frequency band.

[0397] The first determining unit is used to determine the region corresponding to the target frequency band as the first region in the carrier image.

[0398] Optionally, the second processing submodule includes:

[0399] The first segmentation unit is used to segment the first region into multiple first region blocks.

[0400] The first processing unit is used to perform discrete cosine transform processing on each of the first region blocks to obtain the frequency domain coefficient matrix.

[0401] The first construction unit is used to select the mid-frequency coefficients in the frequency domain coefficient matrix and construct the sparse matrix of the first region.

[0402] The second processing unit is used to perform singular value decomposition on the sparse matrix to obtain the first feature matrix of the first region.

[0403] Optionally, the second processing module 1902 includes:

[0404] The first compression and encryption submodule is used to sequentially perform compressed sensing sampling and chaotic encryption processing on the watermark image according to the first key to obtain an encrypted watermark image, wherein the first key is randomly generated according to a chaotic sequence;

[0405] The third processing submodule is used to perform matrix decomposition processing on the encrypted watermark image according to the discrete cosine transform and singular value decomposition techniques to obtain the second feature matrix of the encrypted watermark image.

[0406] Optionally, the first embedding module 1903 includes:

[0407] The first embedding submodule is used to calculate based on the first feature matrix, the second feature matrix and a pre-configured embedding factor, and embed the second feature matrix into the first feature matrix to obtain a third feature matrix;

[0408] The fourth processing submodule is used to perform matrix inverse decomposition and multi-scale inverse transformation on the third feature matrix to obtain the target region, wherein the target region is the first region embedded in the watermark image;

[0409] The first reconstruction submodule is used to reconstruct the carrier image based on the target region to obtain the target image.

[0410] Optionally, the first encryption module 1904 includes:

[0411] The first encryption submodule is used to perform compressed sensing encryption processing on the target image using a first key to generate an encrypted image, wherein the first key is randomly generated based on a chaotic sequence.

[0412] It should be noted that the embodiments of this device are devices corresponding to the embodiments of the above methods. All implementations in the embodiments of the above methods are applicable to the embodiments of this device and can achieve the same technical effect.

[0413] like Figure 20 As shown, this embodiment of the invention also provides an image transmission device, applied to a second device, comprising:

[0414] The first acquisition module 2001 is used to acquire an encrypted image sent by a cloud server, wherein the encrypted image is an encrypted target image and an encrypted watermark image is embedded in the target image;

[0415] The first decryption module 2002 is used to decrypt and reconstruct the encrypted image to obtain the target image;

[0416] The third processing module 2003 is used to perform multi-scale transformation and matrix decomposition processing on the target image to obtain the third feature matrix of the target image;

[0417] The first extraction module 2004 is used to extract the encrypted watermark image from the third feature matrix according to the pre-configured embedding factor, and obtain the second feature matrix corresponding to the watermark image.

[0418] The fourth processing module 2005 is used to perform matrix inverse decomposition and decryption reconstruction on the second feature matrix to obtain the watermark image.

[0419] Optionally, the device further includes:

[0420] The fourth sending module is used to send a query request to the cloud server, the query request being used to request the acquisition of the encrypted image transmitted by the first device;

[0421] The first acquisition module 2001 includes:

[0422] The first receiving submodule is used to receive the encrypted image sent by the cloud server according to the query request.

[0423] Optionally, the first decryption module 2002 includes:

[0424] The first decryption submodule is used to perform image decryption and reconstruction processing on the encrypted image using a second key to obtain the target image, wherein the second key is randomly generated based on a chaotic sequence.

[0425] Optionally, the third processing module 2003 includes:

[0426] The fifth processing submodule is used to perform multi-scale transformation processing on the target image according to the dual-tree complex wavelet transform technique to obtain multiple frequency bands;

[0427] The second determining submodule is used to determine the target region in the target image based on the target frequency band among the multiple frequency bands, wherein the target region is the region embedded in the watermark image;

[0428] The sixth processing submodule is used to perform matrix decomposition processing on the target region according to the discrete cosine transform and singular value decomposition techniques to obtain the third feature matrix of the target region.

[0429] Optionally, the second determining submodule includes:

[0430] The second filtering unit is used to filter the frequency band according to the characteristics of the human eye and the frequency of the frequency band, and determine the frequency band with the lowest frequency as the target frequency band.

[0431] The second determining unit is used to determine the region corresponding to the target frequency band as the target region in the target image.

[0432] Optionally, the sixth processing submodule includes:

[0433] The second segmentation unit is used to segment the target region into multiple target region blocks.

[0434] The third processing unit is used to perform discrete cosine transform processing on each of the target region blocks to obtain the frequency domain coefficient matrix.

[0435] The second construction unit is used to select the mid-frequency coefficients in the frequency domain coefficient matrix to construct the sparse matrix of the target region.

[0436] The fourth processing unit is used to perform singular value decomposition on the sparse matrix to obtain the third feature matrix of the target region.

[0437] Optionally, the fourth processing module 2005 includes:

[0438] The seventh processing submodule is used to perform matrix inverse decomposition on the second feature matrix to obtain the encrypted watermark image;

[0439] The first decryption and reconstruction submodule is used to perform chaotic decryption and compressed sensing recovery processing on the encrypted watermark image in sequence according to the second key to obtain the watermark image, wherein the second key is randomly generated according to the chaotic sequence.

[0440] It should be noted that the embodiments of this device are devices corresponding to the embodiments of the above methods. All implementations in the embodiments of the above methods are applicable to the embodiments of this device and can achieve the same technical effect.

[0441] like Figure 21 As shown, this embodiment of the invention also provides an image transmission device applied to a cloud server, comprising:

[0442] The first receiving module 2101 is used to receive the first encrypted image sent by the first device;

[0443] The first conversion module 2102 is used to perform encryption conversion on the first encrypted image according to the first conversion key corresponding to the first device to obtain the second encrypted image;

[0444] The second receiving module 2103 is used to receive a query request sent by the second device, the query request being used to request the acquisition of an encrypted image transmitted by the first device;

[0445] The second conversion module 2104 is used to perform encryption conversion on the second encrypted image based on the query request and the second conversion key corresponding to the second device to obtain a third encrypted image;

[0446] The third sending module 2105 is used to send the third encrypted image to the second device, wherein the second device is able to decrypt the third encrypted image using the second key of the second device.

[0447] Optionally, the device further includes:

[0448] The second acquisition module is used to acquire a first conversion key, wherein the first conversion key includes a first matrix, the first matrix is ​​randomly generated according to a chaotic sequence, and the first matrix is ​​used to generate a first key, and the first key is used to encrypt and generate the first encrypted image.

[0449] The third acquisition module is used to acquire the second conversion key, wherein the second conversion key includes a second matrix, which is randomly generated according to a chaotic sequence, and the second matrix is ​​used to generate the second key, which is used to decrypt the third encrypted image.

[0450] It should be noted that the embodiments of this device are devices corresponding to the embodiments of the above methods. All implementations in the embodiments of the above methods are applicable to the embodiments of this device and can achieve the same technical effect.

[0451] This invention also provides a network device, including: a processor, a memory, and a program stored in the memory and executable on the processor. When the program is executed by the processor, it implements the image transmission method as described above and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0452] This invention also provides a readable storage medium, comprising: a program stored on the readable storage medium, wherein when the program is executed by a processor, it implements the steps of the image transmission method described in any of the preceding claims and achieves the same technical effect; to avoid repetition, it will not be described again here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0453] This invention also provides a computer program product, including computer instructions. When the computer instructions are executed by a processor, they implement the steps of the image transmission method described in any of the preceding claims and achieve the same technical effect. To avoid repetition, further details are omitted here.

[0454] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0455] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An image transmission method, characterized in that, Applied to the first device, including: The carrier image is subjected to multi-scale transformation and matrix decomposition to obtain the first feature matrix of the first region in the carrier image, wherein the first region is the region used to embed the watermark image. The watermark image is compressed, encrypted, and matrix decomposed to obtain the second feature matrix of the watermark image; Based on the first feature matrix, the second feature matrix, and the pre-configured embedding factor, the watermark image is embedded into the first region of the carrier image to obtain the target image; The target image is subjected to compressed sensing encryption processing to generate an encrypted image; The encrypted image is sent to the cloud server.

2. The image transmission method according to claim 1, characterized in that, The step of performing multi-scale transformation and matrix decomposition on the carrier image to obtain the first feature matrix of the first region in the carrier image includes: Based on the dual-tree complex wavelet transform technique, the carrier image is processed by multi-scale transformation to obtain multiple frequency bands; Based on the target frequency band among the multiple frequency bands, a first region in the carrier image is determined; Based on the discrete cosine transform and singular value decomposition techniques, the first region is subjected to matrix decomposition to obtain the first feature matrix of the first region.

3. The image transmission method according to claim 2, characterized in that, Determining the first region in the carrier image based on the target frequency band among the plurality of frequency bands includes: Based on the characteristics of the human eye and the frequency of the frequency band, the frequency band is filtered, and the frequency band with the lowest frequency is determined as the target frequency band; The region corresponding to the target frequency band is determined as the first region in the carrier image.

4. The image transmission method according to claim 2, characterized in that, The step of performing matrix decomposition processing on the first region based on discrete cosine transform and singular value decomposition techniques to obtain the first feature matrix of the first region includes: The first region is divided into blocks to obtain multiple first region blocks; Perform discrete cosine transform on each of the first region blocks to obtain the frequency domain coefficient matrix; Select the mid-frequency coefficients from the frequency domain coefficient matrix to construct the sparse matrix of the first region; The sparse matrix is ​​subjected to singular value decomposition to obtain the first feature matrix of the first region.

5. The image transmission method according to claim 1, characterized in that, The step of compressing, encrypting, and decomposing the watermark image to obtain the second feature matrix of the watermark image includes: Based on the first key, the watermark image is sequentially subjected to compressed sensing sampling and chaotic encryption processing to obtain an encrypted watermark image, wherein the first key is randomly generated based on a chaotic sequence; The encrypted watermark image is subjected to matrix decomposition processing based on discrete cosine transform and singular value decomposition techniques to obtain the second feature matrix of the encrypted watermark image.

6. The image transmission method according to claim 1, characterized in that, The step of embedding the watermark image into the first region of the carrier image based on the first feature matrix, the second feature matrix, and a pre-configured embedding factor to obtain the target image includes: The third feature matrix is ​​obtained by calculating the second feature matrix into the first feature matrix based on the first feature matrix, the second feature matrix, and the pre-configured embedding factor. The third feature matrix is ​​subjected to inverse matrix decomposition and multi-scale inverse transformation to obtain the target region, wherein the target region is the first region embedded in the watermark image; The carrier image is reconstructed based on the target region to obtain the target image.

7. The image transmission method according to claim 1, characterized in that, The step of performing compressed sensing encryption processing on the target image to generate an encrypted image includes: The target image is compressed sensing encryption using a first key to generate an encrypted image, wherein the first key is randomly generated based on a chaotic sequence.

8. An image transmission method, characterized in that, Applied to a second device, including: Obtain an encrypted image sent by a cloud server, wherein the encrypted image is an encrypted target image, and the target image embeds an encrypted watermark image; The encrypted image is decrypted and reconstructed to obtain the target image; The target image is subjected to multi-scale transformation and matrix decomposition to obtain the third feature matrix of the target image; Based on the pre-configured embedding factor, the encrypted watermark image is extracted from the third feature matrix to obtain the second feature matrix corresponding to the watermark image; The second feature matrix is ​​subjected to matrix inverse decomposition and decryption reconstruction to obtain the watermark image.

9. The image transmission method according to claim 8, characterized in that, The method further includes: Send a query request to the cloud server, the query request being used to request the acquisition of the encrypted image transmitted by the first device; The step of obtaining the encrypted image sent by the cloud server includes: Receive the encrypted image sent by the cloud server in accordance with the query request.

10. The image transmission method according to claim 8, characterized in that, The process of decrypting and reconstructing the encrypted image to obtain the target image includes: The encrypted image is decrypted and reconstructed using a second key to obtain the target image, wherein the second key is randomly generated based on a chaotic sequence.

11. The image transmission method according to claim 8, characterized in that, The step of performing multi-scale transformation and matrix decomposition on the target image to obtain the third feature matrix of the target image includes: The target image is subjected to multi-scale transformation processing based on the dual-tree complex wavelet transform technique to obtain multiple frequency bands; Based on the target frequency band among the multiple frequency bands, a target region in the target image is determined, wherein the target region is the region embedded in the watermark image; The target region is subjected to matrix decomposition based on discrete cosine transform and singular value decomposition techniques to obtain the third characteristic matrix of the target region.

12. The image transmission method according to claim 11, characterized in that, Determining the target region in the target image based on the target frequency band among the plurality of frequency bands includes: Based on the characteristics of the human eye and the frequency of the frequency band, the frequency band is filtered, and the frequency band with the lowest frequency is determined as the target frequency band; The region corresponding to the target frequency band is determined as the target region in the target image.

13. The image transmission method according to claim 11, characterized in that, The step of performing matrix decomposition processing on the target region using discrete cosine transform and singular value decomposition techniques to obtain the third feature matrix of the target region includes: The target region is divided into blocks to obtain multiple target region blocks; Perform discrete cosine transform on each of the target regions to obtain the frequency domain coefficient matrix; Select the mid-frequency coefficients from the frequency domain coefficient matrix to construct the sparse matrix of the target region; The sparse matrix is ​​subjected to singular value decomposition to obtain the third feature matrix of the target region.

14. The image transmission method according to claim 8, characterized in that, The step of performing matrix inverse decomposition and decryption reconstruction on the second feature matrix to obtain the watermark image includes: Perform matrix inverse decomposition on the second feature matrix to obtain the encrypted watermark image; Based on the second key, the encrypted watermark image is sequentially subjected to chaotic decryption and compressed sensing recovery processing to obtain the watermark image, wherein the second key is randomly generated based on a chaotic sequence.

15. An image transmission method, characterized in that, Applied to cloud servers, including: Receive the first encrypted image sent by the first device; Based on the first conversion key corresponding to the first device, the first encrypted image is encrypted and converted to obtain a second encrypted image; Receive a query request sent by a second device, the query request being used to request the acquisition of an encrypted image transmitted by the first device; Based on the query request, the second encrypted image is encrypted and converted according to the second conversion key corresponding to the second device to obtain a third encrypted image; The third encrypted image is sent to the second device, wherein the second device is capable of decrypting the third encrypted image using the second key of the second device.

16. The image transmission method according to claim 15, characterized in that, The method further includes: Obtain a first conversion key, wherein the first conversion key includes a first matrix, the first matrix is ​​randomly generated according to a chaotic sequence, and the first matrix is ​​used to generate a first key, the first key is used to encrypt and generate the first encrypted image; Obtain a second conversion key, wherein the second conversion key includes a second matrix, the second matrix is ​​randomly generated according to a chaotic sequence, and the second matrix is ​​used to generate the second key, the second key being used to decrypt the third encrypted image.

17. A first network device, characterized in that, Includes transceivers and processors, among which, The processor is used for: The carrier image is subjected to multi-scale transformation and matrix decomposition to obtain the first feature matrix of the first region in the carrier image, wherein the first region is the region used to embed the watermark image. The watermark image is compressed, encrypted, and matrix decomposed to obtain the second feature matrix of the watermark image; Based on the first feature matrix, the second feature matrix, and the pre-configured embedding factor, the watermark image is embedded into the first region of the carrier image to obtain the target image; The target image is subjected to compressed sensing encryption processing to generate an encrypted image; The transceiver is used for: The encrypted image is sent to the cloud server.

18. A second network device, characterized in that, Includes transceivers and processors, among which, The transceiver is used for: Obtain an encrypted image sent by a cloud server, wherein the encrypted image is an encrypted target image, and the target image embeds an encrypted watermark image; The processor is used for: The encrypted image is decrypted and reconstructed to obtain the target image; The target image is subjected to multi-scale transformation and matrix decomposition to obtain the third feature matrix of the target image; Based on the pre-configured embedding factor, the encrypted watermark image is extracted from the third feature matrix to obtain the second feature matrix corresponding to the watermark image; The second feature matrix is ​​subjected to matrix inverse decomposition and decryption reconstruction to obtain the watermark image.

19. A third network device, characterized in that, Includes transceivers and processors, among which, The transceiver is used to receive the first encrypted image sent by the first device; The processor is used to perform encryption conversion on the first encrypted image according to the first conversion key corresponding to the first device to obtain a second encrypted image; The transceiver is also used to receive a query request sent by the second device, the query request being used to request the acquisition of an encrypted image transmitted by the first device; The processor is further configured to, based on the query request and according to the second conversion key corresponding to the second device, perform encryption conversion on the second encrypted image to obtain a third encrypted image; The transceiver is also used to send the third encrypted image to the second device, wherein the second device is able to decrypt the third encrypted image using the second key of the second device.

20. An image transmission device, characterized in that, Applied to the first device, including: The first processing module is used to perform multi-scale transformation and matrix decomposition processing on the carrier image to obtain the first feature matrix of the first region in the carrier image, wherein the first region is the region used to embed the watermark image. The second processing module is used to compress, encrypt, and decompose the watermark image to obtain the second feature matrix of the watermark image. The first embedding module is used to embed the watermark image into the first region of the carrier image according to the first feature matrix, the second feature matrix and the pre-configured embedding factor to obtain the target image; The first encryption module is used to perform compressed sensing encryption processing on the target image to generate an encrypted image; The first sending module is used to send the encrypted image to the cloud server.

21. An image transmission device, characterized in that, Applied to a second device, including: The first acquisition module is used to acquire an encrypted image sent by the cloud server, wherein the encrypted image is an encrypted target image and the target image embeds an encrypted watermark image; The first decryption module is used to decrypt and reconstruct the encrypted image to obtain the target image; The third processing module is used to perform multi-scale transformation and matrix decomposition on the target image to obtain the third feature matrix of the target image; The first extraction module is used to extract the encrypted watermark image from the third feature matrix according to the pre-configured embedding factor, and obtain the second feature matrix corresponding to the watermark image. The fourth processing module is used to perform matrix inverse decomposition and decryption reconstruction on the second feature matrix to obtain the watermark image.

22. An image transmission device, characterized in that, Applied to cloud servers, including: The first receiving module is used to receive the first encrypted image sent by the first device; The first conversion module is used to perform encryption conversion on the first encrypted image according to the first conversion key corresponding to the first device to obtain the second encrypted image; The second receiving module is used to receive a query request sent by the second device, the query request being used to request the acquisition of an encrypted image transmitted by the first device; The second conversion module is used to perform encryption conversion on the second encrypted image based on the query request and the second conversion key corresponding to the second device to obtain a third encrypted image; The third sending module is used to send the third encrypted image to the second device, wherein the second device is capable of decrypting the third encrypted image using the second key of the second device.

23. A network device, characterized in that, include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the image transmission method as described in any one of claims 1 to 7, or the image transmission method as described in any one of claims 8 to 14, or the image transmission method as described in any one of claims 15 to 16.

24. A readable storage medium, characterized in that, include: The readable storage medium stores a program that, when executed by a processor, implements the steps of the image transmission method as described in any one of claims 1 to 7, or the steps of the image transmission method as described in any one of claims 8 to 14, or the steps of the image transmission method as described in any one of claims 15 to 16.

25. A computer program product, characterized in that, The method includes computer instructions that, when executed by a processor, implement the steps of the image transmission method as described in any one of claims 1 to 7, or the steps of the image transmission method as described in any one of claims 8 to 14, or the steps of the image transmission method as described in any one of claims 15 to 16.

Images