Picture information embedding method and device, computer equipment and storage medium

Abstract

The invention discloses a picture information embedding method and device, computer equipment and a storage medium, and belongs to the technical field of big data. The method comprises the steps of obtaining a to-be-processed image and to-be-embedded information; performing transform domain processing on the to-be-processed image to obtain a transform domain image; embedding the information to be embedded into the transform domain image to obtain a first embedded image; converting the picture format of the to-be-processed image into a transparent format, and identifying an image transparent area; encoding the to-be-embedded information to obtain a two-dimensional bar code, and embedding the two-dimensional bar code into the image transparent area to obtain a second embedded image; and combining the first embedded image and the second embedded image to generate an information embedded image. The invention further relates to the technical field of block chains, and the embedded information is stored on a block chain network node. Through dual embedding of information in a transform domain and an image transparent area, the stability and anti-interference performance of the embedded information are significantly improved.

Description

Technical Field

[0001] The present application belongs to the field of big data technology, and specifically relates to a method, device, computer equipment and storage medium for embedding image information. Background Art

[0002] Image information embedding refers to embedding specific information or data into an image, so that the image can carry additional information while maintaining the original visual effect. The principle of image information embedding is mainly based on digital image processing technology and information hiding technology. Information is hidden by encoding the information into a specific pattern, texture or color change and embedding it into the pixels of the image. During the decoding process, the embedded information can be extracted from the image through a specific algorithm or key. There are two main types of existing image information embedding technologies: metadata information embedding and pixel-level information embedding. However, these technologies have obvious defects in practical applications.

[0003] Metadata information embedded in EXIF ​​(Exchangeable Image File Format) is a commonly used metadata format stored in image files to record the shooting information of the image (such as geographic location information, camera settings, etc.). However, when transmitting images, EXIF ​​data is often automatically deleted or cropped. This is to protect user privacy and optimize transmission efficiency. Images are usually processed, including removing some unnecessary metadata information. For example, when users transmit images through social platforms, geographic location information and other sensitive data are often stripped, resulting in the loss of information embedded in EXIF.

[0004] Pixel-level information embedding refers to storing data by modifying specific pixel values ​​of an image. The main problem encountered by this technology is that the embedded information is destroyed or difficult to parse due to compression and rotation of the image. When images are uploaded to social media or cloud storage services, they are usually compressed to reduce file size and speed up transmission. However, compression changes the pixel values ​​of the image, especially lossy compression (such as JPEG), which causes the embedded information to be destroyed. In addition, the rotation of the image will also rearrange the pixels, making any hidden information based on specific pixel positions difficult to parse. In general, pixel-level information embedding has poor robustness and it is difficult to maintain the integrity of the embedded information under various image processing operations.

[0005] In summary, existing technologies have obvious deficiencies in embedding image information, especially when facing social media platform transmission, the embedded information is easily lost or damaged. Therefore, there is an urgent need for a more reliable method to embed image information and improve the robustness of image information embedding. Summary of the invention

[0006] The purpose of the embodiments of the present application is to provide a method, apparatus, computer device and storage medium for embedding picture information to solve the technical problem that the embedded information is easily lost or damaged in the existing picture information embedding technology.

[0007] In order to solve the above technical problems, the embodiment of the present application provides a method for embedding image information, which adopts the following technical solution:

[0008] A method for embedding image information, comprising:

[0009] Receive an image information embedding instruction, and obtain the image to be processed and the information to be embedded;

[0010] Perform transform domain processing on the image to be processed to obtain a transform domain image;

[0011] Embedding the information to be embedded into the transform domain image to obtain a first embedded image;

[0012] Converting the image format of the image to be processed into a transparent format, and identifying the image transparent area on the image to be processed in the transparent format;

[0013] Encoding the information to be embedded to obtain a two-dimensional barcode, and embedding the two-dimensional barcode into a transparent area of ​​the image to obtain a second embedded image;

[0014] The first embedded image and the second embedded image are combined to generate an information embedded image.

[0015] Furthermore, the step of performing transform domain processing on the image to be processed to obtain a transform domain image specifically includes:

[0016] Perform image segmentation on the image to be processed to obtain several image blocks;

[0017] Perform discrete cosine transform or discrete wavelet transform on each pixel point on the image block in turn, and obtain the distribution of each pixel point in the new domain space;

[0018] Generate several new image blocks according to the distribution of each pixel in the new domain space;

[0019] Combine several new image blocks to obtain a transform domain image.

[0020] Furthermore, the step of embedding the information to be embedded into the transform domain image to obtain a first embedded image specifically includes:

[0021] After completing the transform domain processing, the image feature distribution of each image block in the new domain space is obtained to obtain the image feature matrix corresponding to each image block;

[0022] Embed the information to be embedded into the image feature matrix to obtain several new feature matrices;

[0023] An inverse transform process is performed based on a number of new feature matrices to obtain a first embedded image, wherein the inverse transform process is an inverse process of the transform domain process.

[0024] Furthermore, the step of embedding the information to be embedded into the image feature matrix to obtain several new feature matrices specifically includes:

[0025] Performing target detection on each image block, and classifying the image blocks based on the target detection results, wherein the categories of the image blocks include subject-type image blocks and background-type image blocks;

[0026] Identify the image feature matrix corresponding to the background image block and obtain several target feature matrices;

[0027] The information to be embedded is sequentially embedded into several target feature matrices to obtain several new feature matrices.

[0028] Furthermore, after the step of performing inverse transformation based on a plurality of new feature matrices to obtain a first embedded image, the method further includes:

[0029] determining whether a watermark is detected in the first embedded image;

[0030] If a watermark is detected in the first embedded image, the embedding position of the information to be embedded in each target feature matrix is ​​adjusted;

[0031] Or the ratio of the information to be embedded in each target feature matrix is ​​continuously adjusted until no watermark can be detected on the first embedded image.

[0032] Furthermore, after the step of converting the image format of the image to be processed into a transparent format, the method further includes:

[0033] Encode the information to be embedded to obtain a two-dimensional barcode;

[0034] Acquire the color information of the two-dimensional barcode, and based on the color information of the two-dimensional barcode, identify a color matching area that matches the color of the two-dimensional barcode on the image to be processed in a transparent format;

[0035] The two-dimensional barcode is embedded into the color matching area to obtain a second embedded image.

[0036] Furthermore, the step of merging the first embedded image and the second embedded image to generate the information embedded image specifically includes:

[0037] Acquire an image pixel matrix of the first embedded image to obtain a first image pixel matrix;

[0038] Acquire an image pixel matrix of the second embedded image to obtain a second image pixel matrix;

[0039] Performing weighted averaging on the first image pixel matrix and the second image pixel matrix to obtain a weighted average pixel matrix;

[0040] Based on the weighted average pixel matrix, an information embedded image is generated.

[0041] In order to solve the above technical problems, the embodiment of the present application further provides a device for embedding picture information, which adopts the following technical solution:

[0042] A device for embedding picture information, comprising:

[0043] An information acquisition module, used to receive an image information embedding instruction and acquire the image to be processed and the information to be embedded;

[0044] A transform domain processing module is used to perform transform domain processing on the image to be processed to obtain a transform domain image;

[0045] A first embedding module, used for embedding the information to be embedded into the transform domain image to obtain a first embedded image;

[0046] A format conversion module, used for converting the image format of the image to be processed into a transparent format, and identifying the image transparent area on the image to be processed in the transparent format;

[0047] A second embedding module is used to encode the information to be embedded to obtain a two-dimensional barcode, and embed the two-dimensional barcode into the transparent area of ​​the image to obtain a second embedded image;

[0048] The image merging module is used to merge the first embedded image and the second embedded image to generate an information embedded image.

[0049] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0050] A computer device comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of any one of the above-mentioned picture information embedding methods when executing the computer-readable instructions.

[0051] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0052] A computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the picture information embedding method described in any one of the above are implemented.

[0053] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0054] The present application discloses a method, device, computer equipment and storage medium for embedding image information, and belongs to the field of big data technology. The present application significantly improves the stability and anti-interference of embedded information by doubly embedding information in the transform domain and the transparent area of ​​the image. First, the information is embedded into the transform domain image, and the frequency domain characteristics of the transform domain are used to make the information more robust, reducing the risk of information loss under compression, scaling or other conventional processing of the image. Secondly, the encoded information generates a two-dimensional barcode and embeds it into the transparent area to ensure the visibility and integrity of the information. Even when part of the image content is damaged, the two-dimensional barcode can still be extracted from the transparent area to restore the embedded information. The solution of the present application provides a multi-level information protection and recovery mechanism, which effectively solves the technical problem that embedded information is easily lost or damaged in traditional methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0056] Figure 1 An exemplary system architecture diagram is shown in which the present application can be applied;

[0057] Figure 2 A flowchart of an embodiment of a method for embedding picture information according to the present application is shown;

[0058] Figure 3 A schematic diagram showing the structure of an embodiment of a device for embedding picture information according to the present application is shown;

[0059] Figure 4 A schematic structural diagram of an embodiment of a computer device according to the present application is shown. DETAILED DESCRIPTION

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0061] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0062] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0063] like Figure 1 As shown, the system architecture 100 may include a terminal device 101, a network 102 and a server 103. The terminal device 101 may be a laptop 1011, a tablet computer 1012 or a mobile phone 1013. The network 102 is used to provide a medium for a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links or optical fiber cables.

[0064] The user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0065] The terminal device 101 can be any electronic device with a display screen and supporting web browsing. In addition to a laptop computer 1011, a tablet computer 1012 or a mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV), a laptop computer, a desktop computer, etc.

[0066] The server 103 may be a server that provides various services, such as a background server that provides support for a web page displayed on the terminal device 101 .

[0067] It should be noted that the picture information embedding method provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the picture information embedding device is generally set in the server / terminal device.

[0068] It should be understood that Figure 1 The numbers of terminal devices, networks and servers in the embodiment are merely illustrative. The above system may have any number of terminal devices, networks and servers according to implementation requirements.

[0069] Continue to refer Figure 2 , shows a flow chart of an embodiment of a method for embedding picture information according to the present application. The picture information embedding method comprises the following steps:

[0070] S201, receiving a picture information embedding instruction, and obtaining an image to be processed and information to be embedded.

[0071] Specifically, first, an external picture information embedding instruction is received, which may include multiple parameters such as the type of embedded information, embedding location, embedding strength, etc. After receiving the instruction, the system starts to obtain the image to be processed and the information to be embedded. The image is usually a standard digital image file, such as JPEG or PNG format, containing rich visual content. The information to be embedded can be various types of data, including text, images, watermarks or digital signatures, etc. The information to be embedded is used as an authentication mark or hidden information of the image. This step ensures that the system can identify the user's embedding needs and locate the embedded content. It also involves the verification of the image and information content, including the recognition of the image format, the integrity check of the information content, etc., to ensure that the system can correctly execute the embedding process and that the embedding process has accurate input parameters and data sources.

[0072] S202, performing transform domain processing on the image to be processed to obtain a transform domain image.

[0073] Specifically, the system performs transform domain processing on the image to be processed to convert the image from the spatial domain to the frequency domain. This transform domain processing can be achieved through methods such as discrete Fourier transform (DFT), discrete cosine transform (DCT) or wavelet transform, which converts the spatial information in the image into characteristic components in the frequency domain. Through this processing, the frequency characteristics of the image can be better utilized to embed information. For example, when using DCT, the image is decomposed into a series of components with different frequencies, and information can be embedded in certain specific frequency regions (usually the mid-frequency region), thereby achieving an effect that is not easily perceived. The core of transform domain processing is to utilize the characteristics of the human visual system that are insensitive to frequency changes, so that the embedded information is both concealed and resistant to common image processing operations (such as compression, filtering, etc.), and the resulting transform domain image allows the system to perform information hiding in the frequency domain, making the embedded information more difficult to be destroyed or removed.

[0074] S203: embed the information to be embedded into the transform domain image to obtain a first embedded image.

[0075] Specifically, the system embeds the information to be embedded into the image that has been processed in the transform domain to generate a first embedded image. In the transform domain, embedded information is usually achieved by adjusting the amplitude or phase of a specific frequency component. This method makes the embedded information more concealed. For example, information can be embedded by modifying the DCT coefficient value in the intermediate frequency region, which does not affect the visual effect of the image and also makes the embedded information more resistant to compression. The information can usually be embedded directly or through modulation, and a certain encoding method can be used to ensure the integrity of the information.

[0076] In order to further improve the robustness, the system can add a certain amount of redundancy during embedding, or use a specific algorithm (such as quantization index modulation or spread spectrum technology) to enhance the anti-interference ability. The first embedded image obtained after this step contains the information to be embedded, and because it is frequency domain embedded, the information is not easily detected and destroyed.

[0077] S204, converting the image format of the image to be processed into a transparent format, and identifying an image transparent area on the image to be processed in the transparent format.

[0078] Specifically, the system first converts the format of the image to be processed into a format that supports transparency (such as PNG) and identifies the transparent areas in the image. The purpose of this operation is to embed additional information in the transparent areas of the image without affecting the main visual content of the image. The identification of transparent areas can be determined by setting a certain threshold to determine which pixels are considered transparent, or by defining specific areas through the image preprocessing stage. The use of transparent formats not only provides concealment for image embedding, but also enhances the flexibility of information embedding, because transparent areas will not interfere with the user's visual experience.

[0079] S205, encoding the information to be embedded to obtain a two-dimensional barcode, and embedding the two-dimensional barcode into the transparent area of ​​the image to obtain a second embedded image.

[0080] Specifically, the system first encodes the information to be embedded and converts it into a recognizable two-dimensional barcode, usually a QR code or Data Matrix code. This type of barcode compresses information into a set of black and white grid points through specific encoding rules. It has high information density and good noise resistance. The encoded two-dimensional barcode can carry a large amount of information and supports embedding in transparent areas of the image. The system then embeds the generated barcode image into the previously identified transparent area, and ensures the compatibility of the barcode with the image visual effect by adjusting properties such as transparency, position and size. By embedding the barcode in the transparent area, the system can realize the addition of information without destroying the image content, and the information can be easily extracted through scanning and decoding.

[0081] S206: Merge the first embedded image and the second embedded image to generate an information embedded image.

[0082] Specifically, the system merges the first embedded image and the second embedded image to generate a final information embedded image containing all embedded information. The merging process is usually based on pixel-level fusion operations to ensure that the transparent areas, frequency domain information and visible content of the two embedded images are fully combined. In actual operation, the merging can be achieved through the fusion of the Alpha channel. For example, for the transparent area barcode part in the second embedded image, it is directly covered to the transparent area position of the first embedded image, while the rest of the part keeps the content of the first embedded image unchanged. This fusion method allows the image to contain both the information embedded in the transform domain and the visible QR code information. After the merging is completed, the information embedded image generated by the system is not only highly robust, but also able to quickly obtain the embedded information by scanning the barcode in specific scenarios.

[0083] In the above embodiments, the present application significantly improves the stability and anti-interference of embedded information by doubly embedding information in the transform domain and the transparent area of ​​the image. First, the information is embedded in the transform domain image, and the frequency domain characteristics of the transform domain are used to make the information more robust, reducing the risk of information loss under compression, scaling or other conventional processing of the image. Secondly, the encoded information generates a two-dimensional barcode and embeds it in the transparent area to ensure the visibility and integrity of the information. Even when part of the image content is damaged, the two-dimensional barcode can still be extracted from the transparent area to restore the embedded information. The solution of the present application provides a multi-level information protection and recovery mechanism, which effectively solves the technical problem that embedded information is easily lost or damaged in traditional methods.

[0084] Furthermore, the step of performing transform domain processing on the image to be processed to obtain a transform domain image specifically includes:

[0085] Perform image segmentation on the image to be processed to obtain several image blocks;

[0086] Perform discrete cosine transform or discrete wavelet transform on each pixel point on the image block in turn, and obtain the distribution of each pixel point in the new domain space;

[0087] Generate several new image blocks according to the distribution of each pixel in the new domain space;

[0088] Combine several new image blocks to obtain a transform domain image.

[0089] In this embodiment, the system first performs image segmentation on the image to be processed, dividing the image into several small blocks, each of which contains a fixed number of pixels. This segmentation can effectively improve the precision of image processing. Then the system performs transform domain processing on the pixels in each image block, which can be achieved through discrete cosine transform (DCT) or discrete wavelet transform (DWT). DCT converts image data from the spatial domain to the frequency domain, and distributes the energy information of each pixel on different frequency components. This distribution characteristic is very suitable for hidden information embedding; while DWT decomposes image data into frequency domain components of different scales and directions, providing multi-resolution information representation, thereby retaining the local and global features of the image. After performing these transform domain processing, the system obtains the distribution of each pixel in the new domain space and generates several new image blocks to reflect the characteristics of each pixel in the frequency domain. Finally, by combining these new image blocks, a transform domain image is reconstructed. In this process, frequency domain processing improves the concealment and robustness of the system when embedding data, because frequency domain information can more effectively resist common interference such as compression and noise reduction, and has less impact on the human visual system, thereby ensuring the visual quality of the image.

[0090] Through the above steps, the image to be processed is processed in the transform domain. The processing method based on the image block and frequency domain distribution characteristics ensures the accuracy and hiding effect of information embedding, and effectively improves the stability and reliability of embedded information. Embedding secret information in the transform domain image makes the embedded information more resistant to interference under various common image processing operations (such as image compression, noise interference, etc.) and does not affect the visual effect of the original image.

[0091] Furthermore, the step of embedding the information to be embedded into the transform domain image to obtain a first embedded image specifically includes:

[0092] After completing the transform domain processing, the image feature distribution of each image block in the new domain space is obtained to obtain the image feature matrix corresponding to each image block;

[0093] Embed the information to be embedded into the image feature matrix to obtain several new feature matrices;

[0094] An inverse transform process is performed based on a number of new feature matrices to obtain a first embedded image, wherein the inverse transform process is an inverse process of the transform domain process.

[0095] In this embodiment, the system first completes the transform domain processing of the image, divides the image to be processed into several image blocks, each image block shows a unique image feature distribution in the new domain space, and the feature distribution can be represented as an image feature matrix. Each feature matrix reflects the structure and detail information of the image block in the frequency domain, such as energy concentration, directional components, etc. Then, according to the embedding strength and concealment requirements, the information to be embedded is embedded in these image feature matrices. This embedding process usually involves fine-tuning the key frequency domain components in the matrix to ensure that the information has a high anti-interference ability while maintaining concealment. After the embedding is completed, the system generates several new feature matrices, which carry the original image information and embedded information, forming an updated version of the image block. Then, the system performs an inverse transform on the new feature matrix, converts the frequency domain information back to the spatial domain, and finally reconstructs the complete image block, and combines the image blocks to obtain the first embedded image. The inverse transform process is the inverse process of the transform domain processing, which ensures that the overall visual effect of the image is not affected, and the embedded information is only stored covertly in the frequency domain.

[0096] Through the above steps, information is embedded in the frequency domain feature matrix, thereby achieving high concealment and anti-interference of the information. Since the information is embedded in the frequency domain and the image is restored by inverse transformation, the embedded information can resist common image processing operations such as compression and noise while maintaining image quality, significantly enhancing the stability of the embedded information.

[0097] Furthermore, the step of embedding the information to be embedded into the image feature matrix to obtain several new feature matrices specifically includes:

[0098] Performing target detection on each image block, and classifying the image blocks based on the target detection results, wherein the categories of the image blocks include subject-type image blocks and background-type image blocks;

[0099] Identify the image feature matrix corresponding to the background image block and obtain several target feature matrices;

[0100] The information to be embedded is sequentially embedded into several target feature matrices to obtain several new feature matrices.

[0101] In this embodiment, the system first divides the image into blocks and performs target detection on each image block to identify the subject and background information in the image. The target detection process can use deep learning methods (such as convolutional neural networks) to automatically identify important elements in the image and classify the image blocks into subject-type image blocks and background-type image blocks. The purpose of this classification is to avoid affecting the visual effect of the subject content as much as possible when embedding information, thereby improving the concealment of embedding and its invisibility to the human eye. Then, the system further identifies the background-type image blocks to obtain the image feature matrix of the background-type image blocks. The background-type image blocks are usually the parts of the image with less changes and fewer details. By identifying these areas and extracting their frequency domain feature matrices, information embedding can be achieved without affecting the subject content. Subsequently, the system embeds the information to be embedded into the feature matrices of these background-type image blocks to generate a new feature matrix. This embedding process can be completed by fine-tuning the feature values ​​of the background area, so that the information is embedded in the image while maintaining concealment and visual consistency. Finally, the system generates a set of new feature matrices that carry the embedded information without destroying the overall visual effect of the image.

[0102] Through the above steps, information is embedded in the background area of ​​the image, thereby achieving a high degree of concealment and robustness of the information. Since the information is embedded in the background feature matrix, the main area of ​​the image is almost unaffected, ensuring the image quality and the integrity of important content.

[0103] Furthermore, after the step of performing inverse transformation based on a plurality of new feature matrices to obtain a first embedded image, the method further includes:

[0104] determining whether a watermark is detected in the first embedded image;

[0105] If a watermark is detected in the first embedded image, the embedding position of the information to be embedded in each target feature matrix is ​​adjusted;

[0106] Or the ratio of the information to be embedded in each target feature matrix is ​​continuously adjusted until no watermark can be detected on the first embedded image.

[0107] In this embodiment, after the system generates the first embedded image, it first performs watermark detection to ensure the integrity and invisibility of the watermark information. The purpose of detecting watermarks is to evaluate the impact of embedded information on the overall effect of the image, especially during the embedding process, any significant watermark traces may reduce the concealment of the embedded information and affect the visual quality of the final image. If watermark information is detected, the system will automatically adjust the embedding position of the embedded information in each target feature matrix. This process reduces the significance of the watermark by redistributing the embedded information within the feature matrix to achieve a higher degree of concealment and better image quality. In addition, the system can further optimize the proportional distribution of the embedded information, that is, adjust the ratio of information between each target feature matrix to ensure that the watermark is visually undetectable. Through continuous iteration, the system can optimize the distribution position and proportion of the embedded information in real time according to the watermark detection results until no watermark traces are detected on the first embedded image, thereby achieving the concealment and stability of the information.

[0108] Through the above steps, a high degree of concealment of the embedded information is achieved, so that the information will not have a significant impact on the visual effect of the image after embedding. The dynamic adjustment mechanism ensures the reasonable distribution of the embedded information in the frequency domain and improves the robustness of the embedded information, making the information both imperceptible and resistant to image processing operations.

[0109] Furthermore, after the step of converting the image format of the image to be processed into a transparent format, the method further includes:

[0110] Encode the information to be embedded to obtain a two-dimensional barcode;

[0111] Acquire the color information of the two-dimensional barcode, and based on the color information of the two-dimensional barcode, identify a color matching area that matches the color of the two-dimensional barcode on the image to be processed in a transparent format;

[0112] The two-dimensional barcode is embedded into the color matching area to obtain a second embedded image.

[0113] In this embodiment, the system first encodes the information to be embedded and generates a two-dimensional barcode containing all necessary information, usually a QR code or other high-density coding format barcode. After generating the barcode, the system extracts the color information contained therein, and forms a set of color information matching the barcode by analyzing the color combination and distribution of each color block in the barcode. These color information will be used to find a suitable embedding area in the image to be processed to ensure that the embedded barcode can be naturally integrated into the image without significantly interfering with the overall visual effect. Next, based on the extracted color information, the system identifies color matching areas on the image to be processed in a transparent format. These areas have colors similar to or adaptable to the color of the barcode, making the embedded barcode more concealed and difficult to be detected by the human eye. After the color matching area is determined, the system embeds the barcode into it to generate a second embedded image containing a two-dimensional code. During the embedding process, the system may further adjust the properties of the barcode such as transparency and edge fusion so that it can be naturally integrated with the details of the image such as color, light and shadow, to achieve a seamless effect of barcode embedding.

[0114] Through the above steps, the QR code can be concealedly embedded in the transparent image, which enhances the visual concealment and natural fusion effect of the embedded information. By using the color matching area, the presence of the barcode will not affect the overall quality of the image and is difficult to detect. At the same time, the stability of the barcode information can be ensured, so that the embedded information can achieve a balance in visual effect and anti-interference ability.

[0115] Furthermore, the step of merging the first embedded image and the second embedded image to generate the information embedded image specifically includes:

[0116] Acquire an image pixel matrix of the first embedded image to obtain a first image pixel matrix;

[0117] Acquire an image pixel matrix of the second embedded image to obtain a second image pixel matrix;

[0118] Performing weighted averaging on the first image pixel matrix and the second image pixel matrix to obtain a weighted average pixel matrix;

[0119] Based on the weighted average pixel matrix, an information embedded image is generated.

[0120] In this embodiment, the system first obtains the pixel matrix of the image from the first embedded image and the second embedded image, respectively, and records them as the first image pixel matrix and the second image pixel matrix. The two matrices contain the image pixel information under different embedding modes. The first image pixel matrix stores the embedded information based on the frequency domain features, while the second image pixel matrix includes the image information embedded in the two-dimensional code. This separate embedding mode ensures the concealment of information in different dimensions. After obtaining the two pixel matrices, the system performs a weighted average operation on the first image pixel matrix and the second image pixel matrix. Specifically, the weighted average can be performed by linearly weighting the corresponding positions of each pixel point of the two matrices, so that the final generated image retains both the frequency domain embedding effect of the first embedded image and the two-dimensional code information embedding effect of the second embedded image. The weighting coefficient can be flexibly adjusted according to the image requirements to further enhance the concealment and stability of the information under the premise of ensuring the visual quality of the image. Finally, the weighted average pixel matrix is ​​used to generate the information embedded image. In this weighting process, the system adjusts the weight of each embedded image according to the requirements of different image contents and information hiding, ensuring that the generated final image can retain the hidden information without affecting the overall visual effect.

[0121] Through the above steps, the weighted average technology is used to realize the embedding of multiple information levels, so that the embedded information is effectively integrated in different dimensions, the concealment and robustness of the information are enhanced, and the embedded information can be retained while improving the image stability, so that the generated image is not only visually natural and imperceptible, but also has good anti-interference performance when facing image processing operations.

[0122] In the above embodiment, the present application discloses a method for embedding image information, which belongs to the field of big data technology. The present application significantly improves the stability and anti-interference of embedded information by doubly embedding information in the transform domain and the transparent area of ​​the image. First, the information is embedded in the transform domain image, and the frequency domain characteristics of the transform domain are used to make the information more robust, reducing the risk of information loss under compression, scaling or other conventional processing of the image. Secondly, the encoded information generates a two-dimensional barcode and embeds it in the transparent area to ensure the visibility and integrity of the information. Even when part of the image content is damaged, the two-dimensional barcode can still be extracted from the transparent area to restore the embedded information. The solution of the present application provides a multi-level information protection and recovery mechanism, which effectively solves the technical problem that embedded information is easily lost or damaged in traditional methods.

[0123] In this embodiment, the electronic device (eg, Figure 1The server shown in the figure) can receive instructions or obtain data through a wired connection or a wireless connection. It should be noted that the above wireless connection method may include but is not limited to 3G / 4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0124] It should be emphasized that in order to further ensure the privacy and security of the above embedded information, the above embedded information can also be stored in a node of a blockchain.

[0125] The blockchain referred to in this application is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0126] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0127] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0128] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0129] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0130] Further references Figure 3 , as a response to the above Figure 2 The present application provides an embodiment of a device for embedding picture information, and the device embodiment is similar to Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0131] like Figure 3 As shown, the picture information embedding device 300 described in this embodiment includes:

[0132] The information acquisition module 301 is used to receive an image information embedding instruction and acquire the image to be processed and the information to be embedded;

[0133] A transform domain processing module 302 is used to perform transform domain processing on the image to be processed to obtain a transform domain image;

[0134] A first embedding module 303, used to embed the information to be embedded into the transform domain image to obtain a first embedded image;

[0135] The format conversion module 304 is used to convert the image format of the image to be processed into a transparent format, and identify the image transparent area on the image to be processed in the transparent format;

[0136] The second embedding module 305 is used to encode the information to be embedded to obtain a two-dimensional barcode, and embed the two-dimensional barcode into the transparent area of ​​the image to obtain a second embedded image;

[0137] The image merging module 306 is configured to merge the first embedded image and the second embedded image to generate an information embedded image.

[0138] Furthermore, the transform domain processing module 302 is specifically used for:

[0139] Perform image segmentation on the image to be processed to obtain several image blocks; perform discrete cosine transform or discrete wavelet transform on each pixel point on the image block in turn, and obtain the distribution of each pixel point in the new domain space; generate several new image blocks according to the distribution of each pixel point in the new domain space; combine several new image blocks to obtain a transform domain image.

[0140] Furthermore, the first embedding module 303 is specifically used for:

[0141] After completing the transform domain processing, the image feature distribution of each image block in the new domain space is obtained to obtain the image feature matrix corresponding to each image block; the information to be embedded is embedded into the image feature matrix to obtain several new feature matrices; based on the several new feature matrices, inverse transform processing is performed to obtain a first embedded image, wherein the inverse transform processing is the inverse processing process of the transform domain processing.

[0142] Furthermore, the first embedding module 303 is further used for:

[0143] Perform target detection on each image block, and classify the image blocks based on the target detection results, wherein the categories of image blocks include subject image blocks and background image blocks; identify the image feature matrix corresponding to the background image blocks to obtain several target feature matrices; embed the information to be embedded into the several target feature matrices in turn to obtain several new feature matrices.

[0144] Furthermore, the image information embedding device 300 further includes a watermark detection module, which is specifically used for:

[0145] Determine whether a watermark is detected in the first embedded image; if a watermark is detected in the first embedded image, adjust the embedding position of the information to be embedded in each target feature matrix; or continue to adjust the ratio of the information to be embedded in each target feature matrix until no watermark is detected on the first embedded image.

[0146] Furthermore, the image information embedding device 300 further includes a color matching module, which is specifically used for:

[0147] The information to be embedded is encoded to obtain a two-dimensional barcode; the color information of the two-dimensional barcode is obtained, and based on the color information of the two-dimensional barcode, a color matching area that matches the color of the two-dimensional barcode is identified on the image to be processed in a transparent format; the two-dimensional barcode is embedded in the color matching area to obtain a second embedded image.

[0148] Furthermore, the image merging module 306 is specifically used for:

[0149] The image pixel matrix of the first embedded image is obtained to obtain a first image pixel matrix; the image pixel matrix of the second embedded image is obtained to obtain a second image pixel matrix; the first image pixel matrix and the second image pixel matrix are weighted averaged to obtain a weighted average pixel matrix; and the information embedded image is generated based on the weighted average pixel matrix.

[0150] In the above embodiment, the present application discloses a device for embedding image information, which belongs to the field of big data technology. The present application significantly improves the stability and anti-interference of embedded information by doubly embedding information in the transform domain and the transparent area of ​​the image. First, the information is embedded in the transform domain image, and the frequency domain characteristics of the transform domain are used to make the information more robust, reducing the risk of information loss under compression, scaling or other conventional processing of the image. Secondly, the encoded information generates a two-dimensional barcode and embeds it in the transparent area to ensure the visibility and integrity of the information. Even when part of the image content is damaged, the two-dimensional barcode can still be extracted from the transparent area to restore the embedded information. The solution of the present application provides a multi-level information protection and recovery mechanism, which effectively solves the technical problem that embedded information is easily lost or damaged in traditional methods.

[0151] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0152] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected and communicated through a system bus. It should be noted that the figure only shows a computer device 4 with a memory 41, a processor 42, and a network interface 43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0153] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0154] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk equipped on the computer device 4, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions of the picture information embedding method, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0155] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run computer-readable instructions stored in the memory 41 or process data, such as computer-readable instructions for running the image information embedding method.

[0156] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0157] In the above embodiment, the present application discloses a computer device, which belongs to the field of big data technology. The present application significantly improves the stability and anti-interference of embedded information by doubly embedding information in the transform domain and the transparent area of ​​the image. First, the information is embedded in the transform domain image, and the frequency domain characteristics of the transform domain are used to make the information more robust, reducing the risk of information loss under compression, scaling or other conventional processing of the image. Secondly, the encoded information generates a two-dimensional barcode and embeds it in the transparent area to ensure the visibility and integrity of the information. Even when part of the image content is damaged, the two-dimensional barcode can still be extracted from the transparent area to restore the embedded information. The solution of the present application provides a multi-level information protection and recovery mechanism, which effectively solves the technical problem that embedded information is easily lost or damaged in traditional methods.

[0158] The present application also provides another implementation, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the above-mentioned image information embedding method.

[0159] In the above embodiment, the present application discloses a computer-readable storage medium, which belongs to the field of big data technology. The present application significantly improves the stability and anti-interference of the embedded information by doubly embedding information in the transform domain and the transparent area of ​​the image. First, the information is embedded in the transform domain image, and the frequency domain characteristics of the transform domain are used to make the information more robust, reducing the risk of information loss under compression, scaling or other conventional processing of the image. Secondly, the encoded information generates a two-dimensional barcode and embeds it in the transparent area to ensure the visibility and integrity of the information. Even when part of the image content is damaged, the two-dimensional barcode can still be extracted from the transparent area to restore the embedded information. The solution of the present application provides a multi-level information protection and recovery mechanism, which effectively solves the technical problem that the embedded information is easily lost or damaged in the traditional method.

[0160] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0161] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0162] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A method for embedding image information, characterized in that: include: Receive an image information embedding instruction, and obtain the image to be processed and the information to be embedded; Performing transform domain processing on the image to be processed to obtain a transform domain image; Embedding the information to be embedded into the transform domain image to obtain a first embedded image; Converting the image format of the image to be processed into a transparent format, and identifying an image transparent area on the image to be processed in the transparent format; Encoding the information to be embedded to obtain a two-dimensional barcode, and embedding the two-dimensional barcode into the transparent area of ​​the image to obtain a second embedded image; The first embedded image and the second embedded image are combined to generate an information embedded image.

2. The method for embedding picture information as claimed in claim 1, characterized in that: The step of performing transform domain processing on the image to be processed to obtain a transform domain image specifically includes: Performing image segmentation on the image to be processed to obtain a plurality of image blocks; Performing discrete cosine transform processing or discrete wavelet transform processing on each pixel point on the image block in turn, and obtaining the distribution of each pixel point in the new domain space; Generating a plurality of new image blocks according to the distribution of each pixel point in the new domain space; A plurality of the new image blocks are combined to obtain the transform domain image.

3. The method for embedding picture information as claimed in claim 2, characterized in that: The step of embedding the information to be embedded into the transform domain image to obtain a first embedded image specifically includes: After completing the transform domain processing, obtaining the image feature distribution of each image block in the new domain space, and obtaining the image feature matrix corresponding to each image block; Embedding the information to be embedded into the image feature matrix to obtain a plurality of new feature matrices; An inverse transformation process is performed based on the several new feature matrices to obtain the first embedded image, wherein the inverse transformation process is an inverse process of the transform domain processing.

4. The method for embedding picture information as claimed in claim 3, characterized in that: The step of embedding the information to be embedded into the image feature matrix to obtain a plurality of new feature matrices specifically includes: Performing target detection on each of the image blocks, and classifying the image blocks based on the target detection results, wherein the categories of the image blocks include subject-type image blocks and background-type image blocks; Identify the image feature matrix corresponding to the background image block to obtain a plurality of target feature matrices; The information to be embedded is sequentially embedded into a plurality of the target feature matrices to obtain a plurality of the new feature matrices.

5. The method for embedding picture information as claimed in claim 4, characterized in that: After the step of performing inverse transformation based on the plurality of new feature matrices to obtain the first embedded image, the method further includes: determining whether a watermark is detected in the first embedded image; If a watermark is detected in the first embedded image, adjusting the embedding position of the information to be embedded in each of the target feature matrices; Or the ratio of the information to be embedded in each of the target feature matrices is continuously adjusted until no watermark can be detected on the first embedded image.

6. The method for embedding picture information as claimed in claim 1, characterized in that: After the step of converting the picture format of the image to be processed into a transparent format, the method further includes: Encoding the information to be embedded to obtain a two-dimensional barcode; Acquire the color information of the two-dimensional barcode, and based on the color information of the two-dimensional barcode, identify a color matching area that matches the color of the two-dimensional barcode on the image to be processed in a transparent format; The two-dimensional barcode is embedded into the color matching area to obtain a second embedded image.

7. The method for embedding picture information as claimed in claim 1, characterized in that: The step of merging the first embedded image and the second embedded image to generate an information embedded image specifically includes: Acquire an image pixel matrix of the first embedded image to obtain a first image pixel matrix; Acquire an image pixel matrix of the second embedded image to obtain a second image pixel matrix; Performing weighted averaging on the first image pixel matrix and the second image pixel matrix to obtain a weighted average pixel matrix; Based on the weighted average pixel matrix, the information embedded image is generated.

8. A device for embedding picture information, characterized in that: include: An information acquisition module, used to receive an image information embedding instruction and acquire the image to be processed and the information to be embedded; A transform domain processing module, used for performing transform domain processing on the image to be processed to obtain a transform domain image; A first embedding module, used for embedding the information to be embedded into the transform domain image to obtain a first embedded image; A format conversion module, used for converting the picture format of the image to be processed into a transparent format, and identifying an image transparent area on the image to be processed in the transparent format; A second embedding module, used for encoding the information to be embedded to obtain a two-dimensional barcode, and embedding the two-dimensional barcode into the transparent area of ​​the image to obtain a second embedded image; The image merging module is used to merge the first embedded image and the second embedded image to generate an information embedded image.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the picture information embedding method according to any one of claims 1 to 7 when executing the computer-readable instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the picture information embedding method according to any one of claims 1 to 7 are implemented.

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