Visual security encryption method and system based on color traffic image, and device

Abstract

The present application relates to the technical field of visual security. Disclosed in the present application are a visual security encryption method and system based on a color traffic image, and a device. The method comprises: performing DWT-based sparsification processing on a color traffic image to be encrypted; then performing three-dimensional spiral scrambling on the sparsified color traffic image; by means of SVD and column vector normalization, optimizing a measurement matrix generated by an improved two-dimensional Logistic chaotic system; then performing compressive sensing on the scrambled image, so as to obtain a compressed image; and finally, performing IWT decomposition on a carrier image, and embedding the compressed image into the least significant bits of the carrier image.

Description

A visual security encryption method, system and device based on color traffic images

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on March 26, 2024, with application number 202410351016.0 and invention name “A visual security encryption method and system based on color traffic images”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of visual safety technology, and in particular to a visual safety encryption method, system and device based on color traffic images. Background Art

[0003] In modern society, the collection and application of traffic images have become a key component in the fields of traffic management, security monitoring, and traffic flow analysis. The acquisition and processing of traffic images are crucial to improving the efficiency of the traffic system, ensuring road safety, and improving urban traffic. However, with the widespread dissemination and sharing of traffic image data, the corresponding information security issues have also attracted increasing attention. The information security issues it faces mainly include the following aspects: (1) Privacy leakage: Traffic images may contain sensitive information such as vehicle license plate numbers, vehicle models, and driver identities. If this information is accessed or leaked by unauthorized persons, it may lead to serious privacy violations. (2) Data tampering: Traffic image data may be tampered with or forged during transmission and storage, which may lead to false traffic incident reports or misleading traffic management systems. (3) Denial of service attacks: Malicious attackers may attempt to prevent the normal operation of traffic monitoring systems through denial of service attacks (e.g., flood attacks), thereby causing traffic congestion and safety risks.

[0004] Image encryption, as an important privacy protection technology, has garnered widespread attention in recent years. Current image encryption techniques often combine other technologies to enhance security, such as chaos theory, DNA coding, optical transformation, and cellular automata. While these techniques can prevent direct access to ciphertext data, noise-like ciphertexts can easily reveal the importance of the protected content and fail to ensure the visual security of the ciphertext. Therefore, improving the visual security of ciphertext to prevent it from being exposed during transmission has become a pressing issue in current encryption technology. Summary of the Invention

[0005] The purpose of this application is to propose a visual security encryption method, system and device based on color traffic images.

[0006] According to a first aspect of an embodiment of the present disclosure, a visual security encryption method based on color traffic images is provided, comprising the following steps: generating parameters and initial values ​​of a two-dimensional chaotic mapping based on color traffic image information; performing sparsification processing on the color traffic image; generating a measurement matrix through a chaotic system, and optimizing the measurement matrix using singular value decomposition and column vector normalization; performing three-dimensional spiral scrambling on the sparsified color traffic image to obtain an encrypted image; compressing the encrypted image using the optimized measurement matrix to obtain a compressed encrypted image; performing an IWT transformation on a carrier image, and embedding the compressed encrypted image into the least significant bits of the high-frequency part (LL, LH, HL) of the carrier image to obtain a final encrypted image.

[0007] According to a second aspect of an embodiment of the present disclosure, a visual security encryption system based on color traffic images is provided, comprising:

[0008] Parameter acquisition module, which generates parameters and initial values ​​of two-dimensional chaotic mapping according to color traffic image information;

[0009] Sparse processing module, which performs sparse processing on color traffic images;

[0010] The optimization module generates a measurement matrix through a chaotic system and optimizes the measurement matrix using singular value decomposition and column vector normalization;

[0011] The scrambling module performs three-dimensional spiral scrambling on the sparse color traffic image to obtain an encrypted image;

[0012] A compression module compresses the encrypted image using the optimized measurement matrix to obtain a compressed encrypted image;

[0013] The encryption module performs IWT transformation on the carrier image and embeds the compressed encrypted image into the least significant bit of the high frequency part (LL, LH, HL) of the carrier image to obtain the final encrypted image.

[0014] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising a memory, a processor, and a computer program stored and running on the memory, wherein when the processor executes the program, the visual security encryption method based on color traffic images is implemented.

[0015] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method for visual security encryption based on color traffic images is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings in the specification, which constitute a part of this application, are used to provide further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute improper limitations on this application.

[0017] Figure 1 is a schematic diagram of a three-dimensional spiral scrambling method;

[0018] Figure 2 is a diagram of the overall encryption framework;

[0019] Figure 3 is a diagram of the decryption framework. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0021] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0022] In the following example, the external key Secretkey is set to: 7f4e9f6847484a87826d30a76b6522479b3e6128df984ad0c91b1711c52176c1, TS = 25, CR = 0.25, the reconstruction algorithm is SL0, and the sampling rate is set to 0.25. The chaotic map used is an improved two-dimensional logistic chaotic map:

[0023] Among them, X n and Y n Represent the two output state variables of the chaotic system.

[0024] Example 1:

[0025] As shown in FIG2 , this embodiment provides a visual security encryption method based on color traffic images, which includes the following steps.

[0026] Step 1: Generate the parameters and initial values ​​of the two-dimensional chaotic map according to the color traffic image information.

[0027] Specifically, a 512-bit hash key KeyVal is obtained from the original color traffic image through the SHA-512 function. The hash key KeyVal is XORed with the external key KeyHex to obtain the key H. The key H is then converted into 8 subkeys k using the following formula:

[0028] in, Represents a bitwise exclusive OR operation, k(i) is the i-th subkey, and H(i) is the i-th key.

[0029] Using the subkey k, we can get the parameters a, delta and initial values ​​key1 and key2 of the chaotic system:

[0030] Among them, row represents the number of rows in the image matrix.

[0031] Step 2: Perform sparse processing on the color traffic image.

[0032] Specifically, the three channels of the original color traffic image are decomposed to obtain channel I r , Channel I g , Channel I b ; For channel I r , Channel I g , Channel I b Perform DWT (Discrete Wavelet Transform) transformation to obtain three sparse matrices, and then perform threshold processing on the three sparse matrices, rewriting the elements below the threshold TS = 25 to 0, and obtaining channel I′ r 、Channel I' g , channel I′ b Among them, I r represents the red channel of the color traffic image, I g represents the green channel of the color traffic image, I b Represents the blue channel of the color traffic image, I′ r represents the red channel of the sparse color traffic image, I' g I′ represents the green channel of the sparse color traffic image. b Represents the blue channel of the thinned color traffic image.

[0033] Step 3: Generate a measurement matrix through the chaotic system and optimize the measurement matrix using singular value decomposition and column vector normalization.

[0034] In an exemplary embodiment, the parameters a, key1, and key2 of the chaotic system are iteratively mapped len+N times. The first N sequences are then discarded to eliminate transient effects of the chaotic mapping, resulting in a chaotic sequence X of length len. The chaotic sequence X is then rewritten as a matrix Φ, and a tensor product operation is performed on the matrix Φ and the reversible matrix to obtain a measurement matrix M: M = Φ × I2, where I2 represents the reversible matrix.

[0035] Specifically, set a = 0.98, use key1 and key2 to iterate the improved two-dimensional logistic-sine chaotic system len + 1000 times, discard the initial 1000 sequences, eliminate the instantaneous effect of the chaotic mapping, obtain the chaotic sequence catSeq of length len, and process it to obtain the chaotic sequence catDeqcon: catDeqcon(i1) = 1 + 2 × catSeq(d × i1); where i1 represents the sequence value and d represents the sampling length.

[0036] Perform SVD (Singular Value Decomposition) on the measurement matrix M to obtain the diagonal elements Σ1 in the decomposed diagonal matrix Σ. Take the average value mean of the elements in Σ1 and assign mean to the diagonal elements of Σ to generate Σ'. Then perform SVD decomposition to obtain the preliminary optimized measurement matrix M'. The significance of this step is to improve the minimum singular value to enhance the column independence of the measurement matrix; M = UΣV T ;

[0037] Among them, U and V are orthogonal matrices, the column vectors of U are left singular vectors, and the column vectors of V are right singular vectors. Σ1=diag(δ1,δ2,...δ r )δ1≥δ2≥...≥δ r >0,δ r Represents the smallest singular value.

[0038] M'=UΣ'V T .

[0039] The optimized measurement matrix M' is normalized by column vectors to obtain the optimized measurement matrix M", which further improves the independence of the column vectors.

[0040] Step 4: Perform three-dimensional spiral scrambling on the sparse color traffic image to obtain the encrypted image.

[0041] Specifically, the iteration length is set, the Lorenz chaotic system is iterated, and the chaotic sequence z1 is obtained. The chaotic sequence z1 is applied to the dynamic Arnlod scrambling, and the three channels I′ of the sparse color traffic image are r , I' g , I′ b Perform dynamic Arnold scrambling to obtain I″ r , I″ g , I″ b Among them, I″ r Represents the red channel of the color traffic image after dynamic Arnold scrambling, I″ gRepresents the green channel of the color traffic image after dynamic Arnold scrambling, I″ b Represents the blue channel of the color traffic image after dynamic Arnold scrambling.

[0042] The three channels of the color traffic image after dynamic Arnold scrambling I″ r , I″ g , I″ b As the three faces of the cube, perform spiral transformation and scrambling starting from the upper right corner to obtain I″′ r , I″′ g , I″′ b Among them, I″′ r Represents the red channel of the color traffic image after spiral scrambling, I″′ g I″′ represents the green channel of the color traffic image after spiral scrambling b The blue channel of the color traffic image after spiral scrambling is shown in Figure 1.

[0043] Step 5: Compress the encrypted image using the optimized measurement matrix to obtain a compressed encrypted image.

[0044] Specifically, the optimized measurement matrix M' is used to measure I'' r , I″′ g , I″′ b Compress it to 1 / 4 of its original size and get the compressed matrix I″″ r , I″″ g , I″″ b Among them, I″″ r Represents the compressed red channel, I″″ g Represents the compressed green channel, I″″ b Represents the compressed blue channel.

[0045] Quantize the compressed matrix and merge the three channels into the encrypted image S;

[0046] Among them, S' r Represents the quantized image, S r Represents the compressed image, min represents the minimum pixel value, and max represents the maximum pixel value.

[0047] Step 6: Perform an IWT (Integer Wavelet Transform) transform on the carrier image, and embed the compressed encrypted image into the least significant bit of the high-frequency part of the carrier image to obtain the final encrypted image.

[0048] Specifically, the three channels of the carrier image C are separated to obtain Cr , C g , C b ; Three channels C of the carrier image r , C g , C b Perform IWT transformation to obtain LL, LH, HL, HH four parts: [LL, LH, HL, HH] = IWT (C). r represents the red channel of the carrier image, C g represents the green channel of the carrier image, C b Represents the blue channel of the carrier image, LL represents low-frequency information, LH represents horizontal high-frequency information, HL represents vertical high-frequency information, and HH represents diagonal high-frequency information.

[0049] The encrypted image S is separated into three channels, Sr, Sg, and Sb. The i2th pixel of Sr is rewritten as the binary representation b8b7b6b5b4b3b2b1. The LH, HL, and HH of Cr are also rewritten in binary format. Subsequently, b2b1 and b4b3 are embedded into the lowest two bits of LH and HL, respectively, and b8b7b6b5 is embedded into the lowest four bits of HH. The other bits remain unchanged, resulting in HH', LH', and HL'. The inverse wavelet transform is then performed to obtain the visually secure carrier image C'. Here, Sr represents the red channel of the encrypted image, Sg represents the green channel of the encrypted image, and Sb represents the blue channel of the encrypted image. HH' represents the embedded diagonal high-frequency information, LH' represents the embedded horizontal high-frequency information, and HL' represents the embedded vertical high-frequency information.

[0050] As shown in FIG3 , the decryption process is the inverse process of the encryption process.

[0051] This application has good visual security, the visual quality of encrypted images is above 42dB, and it can resist various common attacks. It has high practical value in the secure transmission and storage of traffic images.

[0052] Compared with the prior art, the above technical solutions adopted in this application have the following advantages:

[0053] 1. A dynamic three-dimensional spiral scrambling is proposed, which makes full use of the characteristics of color traffic images and realizes the simultaneous encryption of three channels. Combining it with chaotic sequences can further improve the scrambling performance and enhance its randomness.

[0054] 2. A color traffic image encryption and hiding method based on IWT-LSB was designed to hide the encrypted image in a visually meaningful carrier image, and the security performance of the encryption algorithm was further enhanced.

[0055] 3. After the encryption embedding process is completed for the original color traffic image, it can be securely transmitted in the public channel.

[0056] The present application also provides an application scenario, which applies the above-mentioned visual security encryption method based on color traffic images. Specifically: the visual security encryption method based on color traffic images provided in this embodiment can be applied in the transmission process of traffic image data. When transmitting traffic image data, the color traffic image is encrypted using the visual security encryption method provided in this embodiment, and then the encrypted image is transmitted to avoid the appearance of sensitive information such as license plate number, vehicle model, and driver's identity in the encrypted image, thereby avoiding privacy leakage, and preventing the received image from being tampered with or forged, thereby improving the accuracy of traffic incident reporting or traffic management.

[0057] Example 2:

[0058] This embodiment provides a visual security encryption system based on color traffic images, including:

[0059] Parameter acquisition module, which generates parameters and initial values ​​of two-dimensional chaotic mapping according to color traffic image information;

[0060] Sparse processing module, which performs sparse processing on color traffic images;

[0061] The optimization module generates a measurement matrix through a chaotic system and optimizes the measurement matrix using singular value decomposition and column vector normalization;

[0062] The scrambling module performs three-dimensional spiral scrambling on the sparse color traffic image to obtain an encrypted image;

[0063] A compression module compresses the encrypted image using the optimized measurement matrix to obtain a compressed encrypted image;

[0064] The encryption module performs IWT transformation on the carrier image and embeds the compressed encrypted image into the least significant bit of the high-frequency part of the carrier image to obtain the final encrypted image.

[0065] Example 3:

[0066] An electronic device includes a memory, a processor, and a computer program stored and running on the memory, wherein the processor implements the above-mentioned visual security encryption method based on color traffic images when executing the program.

[0067] Example 4:

[0068] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned visual security encryption method based on color traffic images.

[0069] Given the increasingly severe information security challenges facing intelligent transportation systems, this application proposes a visual security image encryption method and system based on a combination of P-tensor product compressed sensing and IWT-LSB embedding for the secure storage and transmission of color traffic images. This method addresses the current problems of insufficient and lack of targeted protection for traffic images, while also addressing issues in current visual security image encryption algorithms, such as insufficient visual security, poor robustness, and low reconstructed image quality. The P-tensor product compressed sensing used in this application can reduce the dimensionality of the measurement matrix, thereby improving transmission efficiency. Furthermore, this method does not require the allocation of additional storage capacity or transmission bandwidth. The proposed IWT-LSB embedding method embeds the encrypted image into the high-frequency region of the carrier image, significantly enhancing the imperceptibility of the encrypted image. This application was simulated in MATLAB 2020a, running on Win10 Intel(R) CPU 2.3GHz, ARM 4.0GB. Tables 1-4 show that the encryption and decryption results obtained in this example are superior to those of other solutions.

[0070] Table 1 PSNR and MSSIM values ​​of simulation results

[0071] Table 2 Correlation coefficients of adjacent pixels in encrypted images

[0072] Table 3 Robustness analysis of images subjected to noise attacks

[0073] Table 4 Robustness analysis of images subjected to shearing attacks

[0074] Those skilled in the art will appreciate that the modules or steps of the present disclosure described above can be implemented using a general-purpose computer device. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. The present disclosure is not limited to any specific combination of hardware and software.

[0075] The above description is merely a preferred embodiment of the present application and is not intended to limit the present application. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

[0076] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Those skilled in the art should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A visual security encryption method based on color traffic images, characterized in that: The following steps are involved: Generate parameters and initial values ​​of two-dimensional chaotic mapping according to color traffic image information; Perform sparse processing on color traffic images; Generate a measurement matrix through a chaotic system and optimize it using singular value decomposition and column vector normalization; Perform three-dimensional spiral scrambling on the sparse color traffic image to obtain the encrypted image; Compressing the encrypted image using the optimized measurement matrix to obtain a compressed encrypted image; The carrier image is transformed by IWT, and the compressed encrypted image is embedded into the least significant bit of the high-frequency part of the carrier image to obtain the final encrypted image.

2. The visual security encryption method based on color traffic images according to claim 1, characterized in that: The parameters and initial values ​​of the two-dimensional chaotic map are generated according to the color traffic image information, including: The 512-bit hash key KeyVal is obtained from the original color traffic image through the SHA-512 function. The hash key KeyVal is XORed with the external key KeyHex to obtain the key H. The key H is then converted into 8 subkeys k using the following formula: in, Represents a bitwise exclusive OR operation; Using the subkey k, the parameters a, delta and initial values ​​key1 and key2 of the chaotic system are obtained; 3. The visual security encryption method based on color traffic images according to claim 1 is characterized in that: Perform sparse processing on color traffic images, including: Decompose the three channels of the original color traffic image to obtain channel I r , Channel I g , Channel I b ; For Channel I r , Channel I g , Channel I b Perform DWT transformation to obtain three sparse matrices respectively, then perform threshold processing on the three sparse matrices, rewrite the elements below the threshold TS=25 to 0, and obtain channel I′ r 、Channel I' g , channel I′ b .

4. The visual security encryption method based on color traffic images according to claim 1, characterized in that: Generate a measurement matrix through a chaotic system, including: Iteratively map the parameters a, key1, key2 of the chaotic system len+N times, then discard the first N sequences to eliminate the transient effect of the chaotic mapping, and obtain a chaotic sequence X of length len; Then rewrite the chaotic sequence X into a matrix Φ, perform a tensor product operation on the matrix Φ and the reversible matrix to obtain the measurement matrix M:

5. The visual security encryption method based on color traffic images according to claim 1 is characterized in that: Optimize the measurement matrix using singular value decomposition and column vector normalization, including: Perform SVD decomposition on the measurement matrix M to obtain the diagonal elements Σ1 in the decomposed diagonal matrix Σ. Take the average value mean of the elements in Σ1 and assign the average value mean to the diagonal elements of the diagonal matrix Σ to generate a diagonal matrix Σ'. Perform SVD decomposition on the diagonal matrix Σ' to obtain the optimized measurement matrix M'. Normalize M' to obtain the measurement matrix M": M=UΣV T ; in, Σ1=diag(δ1,δ2,...δ r )δ1≥δ2≥...≥δ r >0; M'=UΣ'V T 。 6. The visual security encryption method based on color traffic images according to claim 3 is characterized in that: Before performing 3D spiral scrambling, the sparse color traffic image is processed as follows: The chaotic sequence z1 is obtained through the chaotic system and applied to the dynamic Arnold scrambling to the three-channel I′ of the color traffic image. r , I' g , I′ b Perform dynamic Arnold scrambling.

7. The visual security encryption method based on color traffic images according to claim 1, characterized in that: The encrypted image is obtained by performing three-dimensional spiral scrambling on the sparse color traffic image, including: using three channels of the color traffic image as three faces of a cube, and performing spiral transformation scrambling starting from the upper right corner to obtain the encrypted image.

8. The visual security encryption method based on color traffic images according to claim 1 is characterized in that: Perform IWT transformation on the carrier image, including: Get the three channels C of the carrier image C r , C g , C b , for the three channels C of the carrier image r , C g , C b Perform IWT transformation to obtain four parts: LL, LH, HL, and HH; Separate the encrypted image S into three channels Sr, Sg, and Sb, rewrite the i2th pixel of channel Sr into binary representation b8b7b6b5b4b3b2b1, and then rewrite the i2th pixel of channel C r LH, HL, HH are also rewritten in binary format; Subsequently, b2b1 and b4b3 are embedded into the lowest two bits of LH and HL respectively, and b8b7b6b5 is embedded into the lowest four bits of HH; the other bits remain unchanged to obtain HH', LH', HL', and then perform inverse wavelet transform on them to obtain the visually safe carrier image C'.

9. A visual security encryption system based on color traffic images, characterized in that: include: Parameter acquisition module, which generates parameters and initial values ​​of two-dimensional chaotic mapping according to color traffic image information; Sparse processing module, which performs sparse processing on color traffic images; The optimization module generates a measurement matrix through a chaotic system and optimizes the measurement matrix using singular value decomposition and column vector normalization; The scrambling module performs three-dimensional spiral scrambling on the sparse color traffic image to obtain an encrypted image; A compression module compresses the encrypted image using the optimized measurement matrix to obtain a compressed encrypted image; The encryption module performs IWT transformation on the carrier image and embeds the compressed encrypted image into the least significant bit of the high-frequency part of the carrier image to obtain the final encrypted image.

10. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the visual security encryption method based on color traffic images according to any one of claims 1 to 8.

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