Sensitive information secure transmission system

Abstract

The invention discloses a sensitive information secure transmission system, which comprises an information sending end and an information receiving end, and is characterized in that after receiving an original image and sensitive information, the information sending end generates a secret-containing carrier image through multi-scale frequency domain decomposition, sensitive information encryption scattering embedding, wavelet inverse transformation reconstruction and dual-channel processing architecture processing and sends the secret-containing carrier image; and the information receiving end extracts the encrypted sensitive information after receiving the encrypted carrier image, and analyzes and outputs the sensitive information after three-level integrity verification. According to the system, the defects of limited information hiding capacity, weak anti-interference capability, unreliable integrity detection and difficulty in self-adaption of an embedding strategy in the prior art can be overcome, high-density embedding of sensitive information, strong anti-interference performance, complete protection and self-adaption of the embedding strategy are realized, and visual concealment, image quality and transmission safety are balanced.

Description

Technical Field

[0001] This application relates to the field of information transmission technology, and more specifically, to a system for the secure transmission of sensitive information. Background Technology

[0002] With the widespread adoption of mobile internet, uploading images through various apps has become a daily routine for users. This image transmission often involves the transmission of sensitive information such as ID card numbers and bank card numbers. Traditional methods for securely transmitting sensitive information often employ plaintext or separate encryption, which are easily intercepted and cracked by cyberattacks, making it difficult to guarantee information transmission security. Therefore, there is an urgent need for a technical solution that can achieve covert transmission of sensitive information.

[0003] Image steganography has thus become a key research direction. Existing image steganography methods are mainly divided into two categories: traditional spatial or frequency domain processing methods and deep learning methods. Traditional methods, such as least significant bit substitution and wavelet coefficient modulation, have the advantages of simple implementation and low computational cost, but their embedding capacity is severely limited, and the statistical features of the image after embedding information are prone to anomalies, making them extremely vulnerable to statistical analysis attacks and steganalysis detection models, resulting in weak anti-interference capabilities. To improve security and robustness, researchers have introduced deep learning methods, such as using convolutional neural networks to achieve end-to-end steganalysis encoding and decoding, and using generative adversarial networks to generate steganalysis images. These methods have improved the visual naturalness and undetectability of images to some extent, but still face many bottlenecks: problems such as unstable training, lack of controllability of information embedding, and susceptibility to artifacts in generated images; variational autoencoders and self-attention mechanism models generate lower quality images, requiring complex regularization strategies to balance generation and embedding capabilities, and their adversarial robustness is not ideal. Furthermore, existing technologies generally lack reliable information integrity detection mechanisms, making it difficult to cope with information loss caused by common image processing operations such as compression and format conversion. They also cannot adaptively adjust embedding strategies, making it difficult to meet the practical application requirements for high-capacity, high-security, and robust steganography transmission.

[0004] Based on this, this application proposes a system for secure transmission of sensitive information to address the shortcomings of existing technologies and achieve significant benefits in many aspects. Summary of the Invention

[0005] This application provides a secure transmission system for sensitive information. By generating a dense carrier image through multi-scale frequency domain decomposition, encrypted scattering embedding of sensitive information, wavelet inverse transform reconstruction, and dual-channel processing at the information sending end, the information receiving end extracts the information and outputs it after three-level integrity verification. This system can solve the defects of existing technologies, such as limited information hiding capacity, weak anti-interference ability, unreliable integrity detection, and difficulty in adaptive embedding strategies. It achieves high-density embedding of sensitive information, strong anti-interference ability, integrity protection, and adaptive embedding strategy, balancing visual concealment, image quality, and transmission security.

[0006] A system for secure transmission of sensitive information includes an information sending end and an information receiving end;

[0007] The information sending end executes:

[0008] The system receives an original image uploaded by a user and sensitive information to be transmitted. It performs multi-scale frequency domain decomposition on the original image to obtain multiple sub-bands containing different frequency components. It encrypts the sensitive information and scatters and embeds it into the multiple sub-bands to generate encrypted sub-band data.

[0009] The dense subband data is reconstructed by wavelet inverse transform to obtain a spatial domain image. The spatial domain image is then processed using a dual-channel processing architecture to generate a dense carrier image. The visual fidelity channel of the dual-channel processing architecture performs generative adversarial network optimization to improve the visual quality of the image, while the information embedding channel uses a dynamic bit allocation mechanism to enhance the image information embedding capacity and performs differential quantization encoding of the frequency domain coefficients.

[0010] The image containing the encrypted carrier is sent to the information receiving end;

[0011] The information receiving end performs the following:

[0012] Receive the encrypted carrier image and extract the embedded encrypted sensitive information from the encrypted carrier image;

[0013] The encrypted sensitive information is subjected to a three-level integrity check based on the checksum, block hash identifier, and metadata watermark embedded in the encrypted carrier image, and the sensitive information is parsed and output after the check passes.

[0014] Optionally, the original image is decomposed into multiple sub-bands containing different frequency components at multiple scales. The sensitive information is then encrypted and scattered and embedded into the multiple sub-bands to generate encrypted sub-band data, including:

[0015] The original image is subjected to color space conversion, and the luminance component of the image is decomposed into low-frequency sub-band, mid-frequency sub-band and high-frequency sub-band using discrete wavelet transform to obtain the low-frequency sub-band, mid-frequency sub-band and high-frequency sub-band.

[0016] Information embedding strength is assigned according to the frequency characteristics of each sub-band, wherein the lowest embedding strength is used for the low-frequency sub-band, the embedding strength for the mid-frequency sub-band is determined based on local texture complexity, and the highest embedding strength is used for the high-frequency sub-band.

[0017] The sensitive information is encrypted, and the encrypted sensitive information is embedded into the multiple sub-bands by scattering according to the corresponding information embedding intensity to generate encrypted sub-band data.

[0018] Optionally, it also includes introducing dynamic normalization and a dual-channel attention mechanism during the scattering embedding process, wherein:

[0019] The dynamic normalization adjusts the normalization center based on the sub-band mean and achieves a smooth distribution of the encrypted sensitive information in the sub-band through the normalization function;

[0020] In the dual-channel attention mechanism, spatial attention is used to generate a position weight map to highlight important embedding regions, while channel attention is used to dynamically adjust the importance of different channels.

[0021] Optionally, the dynamic normalization processing calculation formula is as follows:

[0022]

[0023] in, The output is the normalized result after dynamic normalization processing, where x is the original data to be processed in the subband, μ is the subband mean, k is an adjustable parameter, and ε is a minimum constant.

[0024] Optionally, the process of generating a dense carrier image from the spatial domain image using the dual-channel processing architecture includes:

[0025] The visual fidelity channel uses generative adversarial networks to optimize image visual quality. The generator refines image details, and the discriminator ensures that the generated image is perceptually consistent with the natural image.

[0026] The information embedding channel adopts a dynamic bit allocation mechanism, which allocates different embedding capacities according to the characteristics of image regions, allocating low embedding capacities to flat regions and high embedding capacities to regions with complex textures.

[0027] Differential quantization encoding is performed on the processed frequency domain coefficients, including fine quantization strategy for low frequency coefficients and coarse quantization strategy for high frequency coefficients;

[0028] A multi-objective loss function is used to jointly optimize the dual-channel processing. The loss function includes dynamic weight fusion loss, multi-scale structure preservation loss, and depth perception consistency loss.

[0029] The dual-channel outputs are weighted and fused to generate a dense carrier image.

[0030] Optionally, the dynamic weight fusion loss is:

[0031]

[0032] The multi-scale structure preservation loss is:

[0033]

[0034] The depth-sensing consistency loss is:

[0035]

[0036] in, For dynamic weight fusion loss, To preserve loss in multi-scale structures, To deeply perceive consistency loss, For dynamic weighting coefficients, These are the pixel values ​​of the original image. Generate pixel values ​​for the image of the model. To generate the local mean of image X, The local mean of the original image Y. To generate the local standard deviation of image X, The local standard deviation of the original image Y. To generate the local covariance between image X and the original image Y, and As a preset constant, This is the feature extraction function.

[0037] Optionally, receiving the encrypted carrier image and extracting the embedded encrypted sensitive information from the encrypted carrier image includes:

[0038] The image containing the dense carrier is received and denoised using Wiener filtering.

[0039] Color space conversion and inverse wavelet transform are performed on the filtered dense carrier image, and regional confidence analysis is performed on the channel sub-bands of the corresponding brightness components after reconstruction.

[0040] For the first region where the regional confidence level is greater than the first set value, the encrypted sensitive information embedded in the first region is directly extracted according to the sub-band embedding rule;

[0041] For the second region whose regional confidence is less than the second set value, a multi-head attention mechanism is used to associate context information to repair the information in the second region. After the repair is completed, the encrypted sensitive information embedded in the second region is extracted according to the sub-band embedding rule.

[0042] Optionally, a three-level integrity check is performed on the encrypted sensitive information based on the checksum, block hash identifier, and metadata watermark embedded in the encrypted carrier image, including:

[0043] Calculate the CRC check value of the encrypted sensitive information, and compare the CRC check value with the check code embedded in the encrypted carrier image to determine the first-level check result;

[0044] The image containing the dense carrier is divided into multiple image blocks according to the image features. The real-time hash value of each image block is calculated and compared with the block hash identifier embedded in the frequency domain coefficient of each image block. The second-level verification result is determined based on the number and distribution of image blocks that do not match.

[0045] Explicit digital watermarks are extracted and verified from the metadata of the image containing the dense carrier, implicit digital watermarks are extracted and verified from the pixel data of the image containing the dense carrier, and the third-level verification result is determined based on the verification results of the two watermarks.

[0046] When the first-level verification result, the second-level verification result, and the third-level verification result are all passed, the third-level integrity verification is determined to be successful.

[0047] Optionally, the tamper location based on block hash identifier includes:

[0048] The image is divided into image blocks of different sizes based on the texture features of the carrier image, and the hash value of each image block is calculated.

[0049] The hash value is encoded and then embedded into the frequency domain coefficients of the corresponding block of the carrier image;

[0050] During the extraction and verification process, the hash value of the image block is recalculated and compared with the extracted embedded hash value to locate the image block that has been tampered with.

[0051] Optional, also includes:

[0052] If the verification fails, the information receiving end locates the tampered area based on the verification result of the three-level integrity verification, and repairs the tampered area using a generative repair network.

[0053] As can be seen from the above technical solutions, the sensitive information secure transmission system provided in this application includes an information sending end and an information receiving end. The information sending end receives the original image and sensitive information, and then processes them through multi-scale frequency domain decomposition, sensitive information encryption scattering embedding, wavelet inverse transform reconstruction, and dual-channel processing architecture to generate a encrypted carrier image and send it. The information receiving end receives the encrypted carrier image, extracts the encrypted sensitive information, and then parses and outputs the sensitive information after passing a three-level integrity check, so as to achieve secure transmission of sensitive information.

[0054] To address the limited information hiding capacity of existing technologies, this system divides the original image into sub-bands of different frequencies through multi-scale frequency domain decomposition. Sensitive information is then encrypted and embedded into multiple sub-bands via scattering. This distributed embedding method avoids statistical feature anomalies caused by traditional centralized embedding. Furthermore, combined with the dynamic bit allocation mechanism of the information embedding channel in the dual-channel processing architecture, the information embedding capacity is significantly improved, enabling high-density embedding of sensitive information. Regarding the weak anti-interference capability of existing technologies, the visual fidelity channel of this system optimizes image visual quality through generative adversarial networks, while the information embedding channel performs differentiated quantization encoding of frequency domain coefficients. The scattering embedding mechanism and dynamic content adaptation algorithm work synergistically, giving the system strong robustness to common image processing operations such as image compression, random cropping, and format conversion, resulting in a low information error rate. To address the inability to reliably detect integrity, the receiver employs a three-level integrity verification system based on checksums, block hash identifiers, and metadata watermarks, forming a comprehensive protection from macro to micro levels. This system not only detects information tampering but also locates damaged areas and, combined with relevant technologies, achieves local information repair. Meanwhile, this system achieves adaptive adjustment of the embedding strategy through dynamic intensity allocation of multi-scale scattering networks and adaptive strategy of dual-channel processing architecture, effectively balancing visual concealment, image quality and transmission security, and solving the shortcomings of existing technologies that are difficult to adaptively adjust the embedding strategy. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0056] Figure 1 This is a schematic diagram of the execution flow of a sensitive information secure transmission system disclosed in an embodiment of this application;

[0057] Figure 2 This is a schematic diagram illustrating a processing method of an information receiving end disclosed in an embodiment of this application. Detailed Implementation

[0058] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0059] This application can be used in a wide variety of general-purpose or special-purpose computing device environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor devices, distributed computing environments including any of the above devices, etc.

[0060] The following section introduces the solution proposed in this application. The technical solution is as follows, and details are provided below.

[0061] Figure 1 This is a schematic diagram of the execution flow of a sensitive information secure transmission system disclosed in an embodiment of this application.

[0062] like Figure 1 As shown, the system may include an information sending end and an information receiving end.

[0063] This steganography system mainly consists of two parts: an information sending end and an information receiving end. The two parts interact with the encrypted carrier image through a data transmission link. The information sending end is responsible for processing the original image, embedding sensitive information, and generating the encrypted carrier image, while the information receiving end is responsible for receiving the encrypted carrier image, extracting sensitive information, and verifying its integrity.

[0064] The information sending end executes:

[0065] Step A1: Receive the original image and sensitive information to be transmitted uploaded by the user, perform multi-scale frequency domain decomposition on the original image to obtain multiple sub-bands containing different frequency components, encrypt the sensitive information and scatter it into the multiple sub-bands to generate encrypted sub-band data.

[0066] Specifically, the information sending end first establishes a user interaction interface to receive two types of core data uploaded by the user. One type is the original image, which serves as the information steganography carrier. It can be in mainstream formats such as BMP, JPEG, and PNG, and the image resolution should be no less than 320×320 pixels to ensure the effectiveness of subsequent multi-scale frequency domain decomposition. The other type is the sensitive information to be transmitted, including but not limited to text, keys, binary files, etc. The size of the sensitive information needs to match the capacity of subsequent sub-band embedding and supports dynamic adjustment to adapt to different scenario requirements.

[0067] After receiving the input data, wavelet transform is used to perform multi-scale frequency domain decomposition on the original image. Specifically, the original image is decomposed into a wavelet decomposition of at least level two, preferably level three, into a low-frequency approximate sub-band and three high-frequency detail sub-bands multiplied by the decomposition level. The high-frequency detail sub-bands correspond to the horizontal, vertical, and diagonal directions, respectively. Each sub-band corresponds to a different frequency component of the original image. The low-frequency sub-band carries the main visual information of the image, while the high-frequency sub-band carries detailed information such as image edges and textures, providing a multi-frequency dimension embedding space for subsequent scattering and embedding of sensitive information.

[0068] Subsequently, sensitive information encryption and scattering embedding are performed. First, the sensitive information is encrypted using a symmetric or asymmetric encryption algorithm to generate encrypted sensitive information. The encryption key is preset by the user or randomly generated by the system to ensure the security of the sensitive information before embedding. Then, the encrypted sensitive information is scattered into multiple subbands obtained earlier according to preset embedding rules. The embedding process follows the principle of embedding more high-frequency subbands and less low-frequency subbands. High-frequency subbands have low visual sensitivity and can carry more encrypted sensitive information, while low-frequency subbands only embed a small amount of key encrypted information because they affect the overall visual quality of the image. Finally, encrypted subband data is generated.

[0069] Step A2: Perform wavelet inverse transform on the dense subband data to reconstruct the spatial domain image. Use a dual-channel processing architecture to process the spatial domain image to generate a dense carrier image. The visual fidelity channel of the dual-channel processing architecture performs generative adversarial network to optimize the image visual quality, the information embedding channel uses a dynamic bit allocation mechanism to enhance the image information embedding capacity, and performs differential quantization encoding of the frequency domain coefficients.

[0070] Specifically, the encrypted subband data generated in step A1 is subjected to an inverse wavelet transform that matches the type of the previous wavelet transform, converting the encrypted subband data in the frequency domain into an initial encrypted image in the spatial domain. This process must ensure the integrity of the subband data and avoid the loss or distortion of embedded encrypted sensitive information due to the inverse transform operation.

[0071] After reconstruction, a dual-channel processing architecture is used to optimize the initial dense image in the spatial domain, simultaneously improving image visual fidelity and information embedding capacity. The core function of the visual fidelity channel is to optimize the visual quality of the initial dense image through a generative adversarial network (GAN). The GAN consists of a generator and a discriminator. The generator is responsible for pixel-level restoration of the initial dense image, reducing distortions such as blockiness and blurring caused by information embedding. The discriminator is responsible for distinguishing the restored dense image from the original image. Through adversarial training between the two, the restored dense image achieves a steganographic effect.

[0072] The information embedding channel achieves its function through a dynamic bit allocation mechanism and frequency domain coefficient differential quantization encoding. The dynamic bit allocation mechanism dynamically allocates information embedding bits based on the texture complexity of each pixel block. Pixel blocks with high texture complexity (such as edges and textured regions) are allocated three to five embedding bits, while pixel blocks with low texture complexity (such as smooth regions) are allocated one to two embedding bits. Frequency domain coefficient differential quantization encoding performs a frequency domain transformation on the spatial domain image again to obtain frequency domain coefficients. After obtaining the frequency domain coefficients, differential quantization is performed according to the absolute value of the coefficients. Frequency domain coefficients with large absolute values ​​(which have a greater impact on the visual image) are quantized with fine-grained quantization, while frequency domain coefficients with small absolute values ​​(which have a smaller impact on the visual image) are quantized with coarse-grained quantization. This further improves the embedding stability of encrypted sensitive information while ensuring visual quality.

[0073] After processing through a dual-channel processing architecture, the optimized spatial domain image is converted to a format consistent with the original image to generate the final encrypted carrier image. This image must meet three conditions: the difference in visual quality between the image and the original image is less than the human visual threshold; the capacity of the embedded encrypted sensitive information meets the user's needs; and there are no obvious distortion traces after the frequency domain coefficients are quantized and encoded.

[0074] Step A3: Send the image containing the encrypted carrier to the information receiving end.

[0075] Specifically, the sending end transmits the encrypted carrier image generated in step A2 to the receiving end via a wired or wireless transmission link. During transmission, the TCP / IP protocol can be used to ensure the reliability of data transmission and avoid problems such as packet loss or damage to the encrypted carrier image during transmission. If there are security risks in the transmission link, additional transport layer encryption can be applied to the encrypted carrier image to further enhance transmission security.

[0076] like Figure 2 As shown, the information receiving end performs the following:

[0077] Step B1: Receive the encrypted carrier image and extract the embedded encrypted sensitive information from the encrypted carrier image.

[0078] Specifically, the information receiving end establishes a transmission interface that matches the information sending end, receives the encrypted carrier image, and first verifies the image format and resolution to confirm that the image has not been corrupted or the resolution is abnormal due to transmission. If the verification fails, a retransmission request is sent to the information sending end.

[0079] The encrypted carrier image that has passed verification is subjected to the inverse operation corresponding to the information sending end to extract encrypted sensitive information. Specifically, the encrypted carrier image is subjected to multi-scale frequency domain decomposition in the same way as the decomposition method of the sending end to obtain multiple sub-bands containing embedded information. Then, according to the embedding rules preset by the sending end (which can be pre-shared through the secure channel or embedded in the image metadata), the encrypted sensitive information embedded by scattering is extracted from each sub-band. The extraction process needs to match the embedding bit allocation strategy of the sending end to ensure that the extracted encrypted sensitive information is complete and without errors.

[0080] Receiving the encrypted carrier image and extracting the embedded encrypted sensitive information from the encrypted carrier image specifically includes:

[0081] ① Receive the image containing the dense carrier and perform noise reduction processing using Wiener filtering;

[0082] ② Perform color space conversion and inverse wavelet transform on the filtered dense carrier image, and perform regional confidence analysis on the channel sub-bands of the corresponding brightness components after reconstruction;

[0083] ③ For the first region where the regional confidence level is greater than the first set value, directly extract the encrypted sensitive information embedded in the first region according to the sub-band embedding rule;

[0084] ④ For the second region where the regional confidence is less than the second set value, a multi-head attention mechanism is used to associate context information to repair the information in the second region. After the repair is completed, the encrypted sensitive information embedded in the second region is extracted according to the sub-band embedding rule.

[0085] First, Wiener filtering is applied to the received dense carrier image for denoising. Wiener filtering can adaptively adjust the filtering parameters according to the statistical characteristics of image noise, effectively suppressing interference such as Gaussian noise introduced during transmission, while preserving the details of sub-bands embedding sensitive information in the image to the greatest extent, avoiding damage to the embedded information during denoising. Second, color space conversion and inverse wavelet transform are performed on the filtered dense carrier image. Color space conversion requires converting the image from the original RGB color space to the YUV color space. Then, an inverse wavelet transform matching the wavelet transform type of the information transmitter is performed on the luminance component (Y component) of the converted image. Since the luminance component carries the main structural information of the image, and sensitive information is mostly embedded in the sub-bands corresponding to the luminance component, processing the luminance component can improve the accuracy of information extraction. After the inverse wavelet transform is completed, regional confidence analysis is performed on the channel sub-bands corresponding to the reconstructed luminance component. Regional confidence is determined by comprehensively calculating the variance of pixel gray values, gradient change rate, and correlation with adjacent sub-bands within the sub-band, and is used to evaluate the integrity and reliability of the embedded information in each region of the sub-band.

[0086] Based on the regional confidence analysis results, the embedded encrypted sensitive information is extracted by region: For the first region with a regional confidence greater than a first set value (the first set value is dynamically adjusted according to the image noise level and sub-band type), the embedded sensitive information in this region is less affected by noise interference and has high integrity. The encrypted sensitive information in the first region is directly extracted according to the sub-band embedding rules preset by the information sender. The extraction process does not require additional repair operations to ensure extraction efficiency. For the second region with a regional confidence less than a second set value, the embedded sensitive information may be missing or distorted due to noise interference or transmission loss. It is necessary to first use a multi-head attention mechanism to associate contextual information to repair the information in the second region. The multi-head attention mechanism can simultaneously focus on the pixel features and frequency domain coefficient correlation between the second region and the adjacent high-confidence region. By capturing contextual dependency information of different dimensions through multiple sets of attention heads, the missing embedded information is completed and corrected. After the repair is completed, the encrypted sensitive information in the second region is extracted according to the sub-band embedding rules to ensure the integrity of the extracted information.

[0087] In addition, for transitional regions where the confidence level is between the first and second set values, a lightweight repair mechanism such as a single-head attention mechanism can be used before information extraction is performed to balance extraction accuracy and processing efficiency. Finally, the encrypted sensitive information extracted from each region is integrated to form a complete set of encrypted sensitive information, which prepares for subsequent integrity verification.

[0088] Step B2: Perform a three-level integrity check on the encrypted sensitive information based on the checksum, block hash identifier, and metadata watermark embedded in the encrypted carrier image, and parse and output the sensitive information after the check passes.

[0089] Specifically, based on the three types of verification information pre-embedded in the encrypted carrier image, a three-level integrity check is performed on the extracted encrypted sensitive information, namely:

[0090] Level 1 verification (checksum): The information receiving end calculates the CRC checksum of the extracted encrypted sensitive information and compares it with the checksum embedded in the carrier image. If they match, the verification passes; otherwise, an error message is displayed indicating an information integrity problem.

[0091] The second-level verification (block hash identifier): The image is adaptively divided into blocks according to the characteristics of the carrier image, and the hash value of each block is calculated in real time. It is compared with the block hash identifier embedded in the frequency domain coefficient of the corresponding block. If the number of inconsistent blocks is within the preset threshold and they are scattered, it passes; if they exceed the threshold or are concentrated, it is determined that there is tampering and the verification fails.

[0092] Level 3 verification (metadata watermark): Explicit watermark is extracted from the carrier image metadata, and implicit watermark is extracted from the pixel data. If both are verified, the level 3 verification is passed, further confirming that the information has not been tampered with.

[0093] Only when all three integrity checks pass is the extracted encrypted sensitive information deemed complete and unaltered. At this point, a decryption algorithm matching the sending end is used to decrypt the encrypted sensitive information, obtaining the original sensitive information to be transmitted, which is then output through the user interface. If any level of check fails, a check failure message is output, and the user can choose to re-receive the encrypted carrier image or terminate the parsing process.

[0094] As can be seen from the above technical solutions, the sensitive information secure transmission system provided in this application includes an information sending end and an information receiving end. The information sending end receives the original image and sensitive information, and then processes them through multi-scale frequency domain decomposition, sensitive information encryption scattering embedding, wavelet inverse transform reconstruction, and dual-channel processing architecture to generate a encrypted carrier image and send it. The information receiving end receives the encrypted carrier image, extracts the encrypted sensitive information, and then parses and outputs the sensitive information after passing a three-level integrity check, so as to achieve secure transmission of sensitive information.

[0095] To address the limited information hiding capacity of existing technologies, this system divides the original image into sub-bands of different frequencies through multi-scale frequency domain decomposition. Sensitive information is then encrypted and embedded into multiple sub-bands via scattering. This distributed embedding method avoids statistical feature anomalies caused by traditional centralized embedding. Furthermore, combined with the dynamic bit allocation mechanism of the information embedding channel in the dual-channel processing architecture, the information embedding capacity is significantly improved, enabling high-density embedding of sensitive information. Regarding the weak anti-interference capability of existing technologies, the visual fidelity channel of this system optimizes image visual quality through generative adversarial networks, while the information embedding channel performs differentiated quantization encoding of frequency domain coefficients. The scattering embedding mechanism and dynamic content adaptation algorithm work synergistically, giving the system strong robustness to common image processing operations such as image compression, random cropping, and format conversion, resulting in a low information error rate. To address the inability to reliably detect integrity, the receiver employs a three-level integrity verification system based on checksums, block hash identifiers, and metadata watermarks, forming a comprehensive protection from macro to micro levels. This system not only detects information tampering but also locates damaged areas and, combined with relevant technologies, achieves local information repair. Meanwhile, this system achieves adaptive adjustment of the embedding strategy through dynamic intensity allocation of multi-scale scattering networks and adaptive strategy of dual-channel processing architecture, effectively balancing visual concealment, image quality and transmission security, and solving the shortcomings of existing technologies that are difficult to adaptively adjust the embedding strategy.

[0096] In some embodiments of this application, the process of performing multi-scale frequency domain decomposition on the original image in step A1 to obtain multiple sub-bands containing different frequency components, encrypting the sensitive information and scattering it into the multiple sub-bands to generate encrypted sub-band data is described. Specifically, it may include:

[0097] Step A11: Perform color space conversion on the original image and use discrete wavelet transform to decompose the brightness component of the image into low-frequency sub-band, mid-frequency sub-band and high-frequency sub-band.

[0098] Step A12: Allocate information embedding strength according to the frequency characteristics of each sub-band, wherein the lowest embedding strength is used for the low-frequency sub-band, the embedding strength is determined based on the local texture complexity for the mid-frequency sub-band, and the highest embedding strength is used for the high-frequency sub-band.

[0099] Step A13: Encrypt the sensitive information and embed the encrypted sensitive information into the multiple sub-bands according to the corresponding information embedding intensity scattering to generate encrypted sub-band data.

[0100] Specifically, the original image is first converted to a color space more suitable for frequency domain processing. Then, discrete wavelet transform is used to decompose the image's luminance components into multiple levels. Through this decomposition, the image's luminance components can be broken down into low-frequency, mid-frequency, and high-frequency sub-bands, each corresponding to different frequency information components in the image.

[0101] The information embedding intensity is allocated according to the frequency characteristics of each sub-band. The low-frequency sub-band carries the main visual information of the image and has a greater impact on the overall visual perception of the image, so it adopts the lowest embedding intensity. The embedding intensity of the mid-frequency sub-band is determined according to its local texture complexity. For areas with more complex textures, the embedding intensity can be appropriately increased, while for areas with relatively simple textures, the embedding intensity is decreased. The high-frequency sub-band has less visual impact on the image and can carry more information, so it adopts the highest embedding intensity.

[0102] First, the sensitive information is encrypted to ensure the security of the information during embedding and transmission. Then, the encrypted sensitive information is scattered and embedded into multiple sub-bands according to the information embedding strength corresponding to each sub-band as determined in the previous process, and finally, encrypted sub-band data is generated.

[0103] Furthermore, this application also includes introducing a dynamic normalization and dual-channel attention mechanism during the scattering embedding process, wherein:

[0104] The dynamic normalization adjusts the normalization center based on the sub-band mean and achieves a smooth distribution of the encrypted sensitive information in the sub-band through the normalization function;

[0105] In the dual-channel attention mechanism, spatial attention is used to generate a position weight map to highlight important embedding regions, while channel attention is used to dynamically adjust the importance of different channels.

[0106] Specifically, dynamic normalization adjusts the normalization center based on the sub-band mean, and then uses a normalization function to ensure a smooth distribution of encrypted sensitive information within the sub-bands, avoiding significant image distortion caused by concentrated information embedding. In the dual-channel attention mechanism, spatial attention generates a positional weight map to highlight important regions in the sub-bands suitable for information embedding, allowing more information to be embedded in these regions. Channel attention dynamically adjusts the importance of different channels, rationally allocating information embedding resources based on each channel's impact on information carrying capacity and image quality, further improving steganography effectiveness.

[0107] The formula for dynamic normalization is:

[0108]

[0109] in, The output is the normalized result after dynamic normalization processing, where x is the original data to be processed in the subband, μ is the subband mean, k is an adjustable parameter, and ε is a minimum constant.

[0110] In some embodiments of this application, the process of the dual-channel processing architecture described in step A2 for processing the spatial domain image to generate a dense carrier image is described, which may specifically include:

[0111] Step A21: The visual fidelity channel uses generative adversarial networks to optimize the visual quality of the image. The generator refines the image details, and the discriminator ensures that the generated image is perceptually consistent with the natural image.

[0112] Specifically, the visual fidelity channel utilizes generative adversarial networks (GANs) to optimize the visual quality of images. The generator refines the details of the spatial domain image, correcting distortions such as blurring and blockiness that may arise from information embedding, and enhancing the image's texture and edge sharpness. The discriminator, by learning the feature distribution of natural images, discriminates the image output by the generator, ensuring that the optimized image visually matches the natural image, achieving an effect that is difficult for the human eye to distinguish, thus maintaining the stealth of the steganographic image.

[0113] Step A22: The information embedding channel adopts a dynamic bit allocation mechanism, allocating different embedding capacities according to the characteristics of the image region. Low embedding capacity is allocated to flat regions, and high embedding capacity is allocated to regions with complex textures.

[0114] Specifically, the information embedding channel employs a dynamic bit allocation mechanism, allocating corresponding embedding capacities based on the characteristics of different regions of the image. For flat regions in the image, due to their gradual pixel value changes and high visual sensitivity, a lower embedding capacity is allocated to avoid significant impact on the image's visual quality from information embedding. Conversely, for regions with complex textures, due to their disordered pixel distribution and lower sensitivity of the human eye to detail changes, a higher embedding capacity is allocated to maximize information carrying capacity while ensuring visual concealment.

[0115] Step A23: Perform differential quantization encoding on the processed frequency domain coefficients, including using a fine quantization strategy for low-frequency coefficients and a coarse quantization strategy for high-frequency coefficients.

[0116] Specifically, differentiated quantization encoding is applied to the frequency domain coefficients after initial dual-channel processing. For low-frequency coefficients, which directly affect the overall contour and main visual information of the image, a fine quantization strategy is adopted, using a smaller quantization step size to reduce coefficient distortion and ensure that the basic visual features of the image are not destroyed. For high-frequency coefficients, which mainly correspond to the details and textures of the image and have a smaller impact on the overall visual effect, a coarse quantization strategy is adopted, using a larger quantization step size to sacrifice some details within an acceptable range in exchange for more information embedding space.

[0117] Step A24: Use a multi-objective loss function to jointly optimize the dual-channel processing. The loss function includes dynamic weight fusion loss, multi-scale structure preservation loss, and depth perception consistency loss.

[0118] Specifically, a multi-objective loss function is employed to jointly optimize the dual-channel processing to balance visual quality and information embedding performance. This loss function includes dynamic weight fusion loss, multi-scale structure preservation loss, and depth-perception consistency loss: the dynamic weight fusion loss dynamically adjusts the weight ratio of visual fidelity and information embedding based on the real-time effect of dual-channel processing, ensuring synergistic optimization; the multi-scale structure preservation loss maintains the structural consistency of the image at various scales by comparing the structural features of the images before and after processing at different scales; and the depth-perception consistency loss, based on image features extracted by the deep neural network, ensures that the processed image maintains consistency with the original image in high-level semantic perception.

[0119] The dynamic weight fusion loss is:

[0120]

[0121] The multi-scale structure preservation loss is:

[0122]

[0123] The depth-sensing consistency loss is:

[0124]

[0125] in, For dynamic weight fusion loss, To preserve loss in multi-scale structures, To deeply perceive consistency loss, For dynamic weighting coefficients, These are the pixel values ​​of the original image. Generate pixel values ​​for the image of the model. To generate the local mean of image X, The local mean of the original image Y. To generate the local standard deviation of image X, The local standard deviation of the original image Y. To generate the local covariance between image X and the original image Y, and As a preset constant, This is the feature extraction function.

[0126] Step A25: Weighted fusion of the dual-channel output results to generate a dense carrier image.

[0127] Specifically, the optimized image output from the visual fidelity channel and the dense image output from the information embedding channel are weighted and fused. During the fusion process, the weights are dynamically adjusted according to the visual importance and information embedding requirements of different regions. Higher weights are given to the visual fidelity result in visually sensitive regions and higher weights are given to the information embedding result in information-dense regions, ultimately generating a dense carrier image that simultaneously satisfies high visual quality and high information capacity.

[0128] In some embodiments of this application, the process of performing a three-level integrity check on the encrypted sensitive information based on the checksum, block hash identifier, and metadata watermark embedded in the encrypted carrier image in step B2 is described, which may specifically include:

[0129] Step B21: Calculate the CRC check value of the encrypted sensitive information, and compare the CRC check value with the check code embedded in the encrypted carrier image to determine the first-level check result;

[0130] Step B22: Divide the image containing the dense carrier into multiple image blocks according to the image features, calculate the real-time hash value of each image block and compare it with the block hash identifier embedded in the frequency domain coefficient of each image block, and determine the second-level verification result based on the number and distribution of image blocks that do not match.

[0131] Step B23: Extract and verify explicit digital watermarks from the metadata of the image containing the cryptic carrier, extract and verify implicit digital watermarks from the pixel data of the image containing the cryptic carrier, and determine the third-level verification result based on the verification results of the two watermarks.

[0132] Step B24: When the first-level verification result, the second-level verification result, and the third-level verification result are all passed, the three-level integrity verification is determined to be passed.

[0133] Specifically, in the first-level verification, the CRC check value of the extracted encrypted sensitive information is first calculated, which reflects the overall integrity of the information. Then, this calculated CRC check value is compared with the check code pre-embedded in the encrypted carrier image. If the two match perfectly, the first-level verification result is passed; if there is a difference, the first-level verification fails, indicating that the encrypted sensitive information may be completely damaged or tampered with.

[0134] In the second-level verification, based on image features such as texture distribution and edge characteristics of the dense carrier image, it is adaptively divided into multiple image blocks. The size of image blocks in different regions can be flexibly adjusted according to feature complexity. Then, a real-time hash value is calculated for each image block. This value accurately represents the content features of the current image block, and it is compared one by one with the pre-embedded block hash identifier in the frequency domain coefficients of each image block. The second-level verification result is determined based on the proportion and spatial distribution of inconsistent image blocks in the comparison results: if the number of inconsistent image blocks is within a preset threshold and is dispersed, it can be judged as slight interference, and the second-level verification passes; if the number exceeds the threshold or is concentrated, the second-level verification fails.

[0135] In the third-level verification, an explicit digital watermark is extracted from the metadata of the image containing the encrypted carrier. This watermark is directly associated with the basic features of the sensitive information, and its validity is verified through a preset algorithm. Simultaneously, an implicit digital watermark is extracted from the pixel data of the image containing the encrypted carrier. This watermark is deeply embedded in the pixel value changes and is extracted and verified through a decoding process. The verification results of the explicit and implicit digital watermarks are combined to determine the third-level verification result. The third-level verification is considered passed only if both are verified to be valid.

[0136] Only when the results of the first-level verification, the second-level verification, and the third-level verification are all passed can it be determined that the three-level integrity verification has passed as a whole, indicating that the extracted encrypted sensitive information is complete and has not been illegally tampered with.

[0137] Furthermore, the tamper location based on block hash identifiers includes:

[0138] The image is divided into image blocks of different sizes based on the texture features of the carrier image, and the hash value of each image block is calculated.

[0139] The hash value is encoded and then embedded into the frequency domain coefficients of the corresponding block of the carrier image;

[0140] During the extraction and verification process, the hash value of the image block is recalculated and compared with the extracted embedded hash value to locate the image block that has been tampered with.

[0141] Specifically, the image is first divided into blocks based on the texture features of the carrier image. For regions with dense texture and rich details (such as complex patterns and areas with intersecting edges), smaller image blocks are created to improve the accuracy of subsequent tamper location. For regions with smooth texture and simple information (such as solid color backgrounds and areas with smooth transitions), larger image blocks are created to reduce computational resource consumption and improve processing efficiency. This adaptive partitioning method matches the size of the image blocks with the complexity of the region's information, balancing location accuracy and processing performance.

[0142] Next, for each image block after division, its hash value is calculated. This hash value is generated by calculating multi-dimensional information such as pixel grayscale distribution, local gradient changes, and texture features of the image block. It is unique, that is, the hash value remains consistent when the content of the same image block does not change, and the hash value will be significantly different when the content is tampered with, thus accurately representing the original state of the image block.

[0143] Subsequently, the calculated hash values ​​of each image patch are encoded and converted into a binary data stream suitable for embedding. The encoding process must ensure the integrity and anti-interference of the hash information before embedding the encoded hash data into the frequency domain coefficients of the corresponding image patch. Frequency domain coefficients are chosen as the embedding location because the frequency domain mainly carries detailed information about the image; embedding hash identifiers here has a relatively small impact on the visual quality of the image, while also forming a close association with the content features of the image patch.

[0144] During the extraction and verification phase, the received image containing the encrypted carrier is re-divided into image blocks according to the same rules, and the real-time hash value of each image block is recalculated. Simultaneously, pre-embedded hash identifiers are extracted from the frequency domain coefficients of each image block, and the real-time calculated hash value is compared one by one with the extracted embedded hash value. If they match, it indicates that the image block has not been tampered with; if they do not match, the image block can be directly located as the tampered area, achieving accurate identification of the tampering location.

[0145] Furthermore, considering that if the Level 3 integrity check fails, the image containing the encrypted carrier may be partially tampered with or have its information damaged, making it impossible to properly parse sensitive information, it is necessary to restore the image integrity through technical means to attempt to extract the information again. This application also includes the following processing steps:

[0146] If the verification fails, the information receiving end locates the tampered area based on the verification result of the three-level integrity verification, and repairs the tampered area using a generative repair network.

[0147] Specifically, the results of the first-level verification are combined to determine whether there is an overall information deviation. Based on the inconsistent image blocks identified in the second-level block hash comparison, the scope of the initial tampered area is determined. At the same time, the results of the third-level watermark verification are referenced. If the explicit or implicit digital watermark fails to be extracted or the verification is abnormal in a specific area, the boundary of the tampered area is further narrowed. Finally, the multi-dimensional verification data is integrated to accurately determine the specific location, size and degree of tampering of the tampered area.

[0148] After locating the tampered area, the information receiving end invokes a generative inpainting network to repair it. This network uses the untampered normal image area as a reference, first extracting texture features, color distribution, and frequency domain coefficient patterns from the surrounding normal area, and inputting these as constraints into the network. The generator module, based on these constraints and learning from natural image structures, generates repair content with high visual integration with the surrounding area, filling the information gaps in the tampered area. The discriminator module then judges the consistency between the generated repair content and the normal image area. Through adversarial training between the generator and discriminator, the repair effect is iteratively optimized to ensure that the repaired area has no obvious visual traces and that the frequency domain coefficient distribution conforms to the characteristics of the original image.

[0149] After the repair is completed, the information receiving end will re-perform a three-level integrity check on the repaired encrypted carrier image. If the check passes, the encrypted sensitive information will be extracted and parsed according to the normal process. If the check still fails after repair, it indicates that the degree of tampering exceeds the repair capability. The information receiving end will output a failure message to the user and can selectively save the tampering location results and repair logs to provide data support for subsequent problem investigation.

[0150] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0151] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0152] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A system for secure transmission of sensitive information, characterized in that, This includes the information sending end and the information receiving end; The information sending end executes: The system receives an original image uploaded by a user and sensitive information to be transmitted. It performs multi-scale frequency domain decomposition on the original image to obtain multiple sub-bands containing different frequency components. It encrypts the sensitive information and scatters and embeds it into the multiple sub-bands to generate encrypted sub-band data. The dense subband data is reconstructed by wavelet inverse transform to obtain a spatial domain image. The spatial domain image is then processed using a dual-channel processing architecture to generate a dense carrier image. The visual fidelity channel of the dual-channel processing architecture performs generative adversarial network optimization to improve the visual quality of the image, while the information embedding channel uses a dynamic bit allocation mechanism to enhance the image information embedding capacity and performs differential quantization encoding of the frequency domain coefficients. The image containing the encrypted carrier is sent to the information receiving end; The information receiving end performs the following: Receive the encrypted carrier image and extract the embedded encrypted sensitive information from the encrypted carrier image; The encrypted sensitive information is subjected to a three-level integrity check based on the checksum, block hash identifier, and metadata watermark embedded in the encrypted carrier image, and the sensitive information is parsed and output after the check passes.

2. The system according to claim 1, characterized in that, The original image is decomposed into multiple sub-bands containing different frequency components at multiple scales. The sensitive information is encrypted and scattered and embedded into the multiple sub-bands to generate encrypted sub-band data, including: The original image is subjected to color space conversion, and the luminance component of the image is decomposed into low-frequency sub-band, mid-frequency sub-band and high-frequency sub-band using discrete wavelet transform to obtain the low-frequency sub-band, mid-frequency sub-band and high-frequency sub-band. Information embedding strength is assigned according to the frequency characteristics of each sub-band, wherein the lowest embedding strength is used for the low-frequency sub-band, the embedding strength for the mid-frequency sub-band is determined based on local texture complexity, and the highest embedding strength is used for the high-frequency sub-band. The sensitive information is encrypted, and the encrypted sensitive information is embedded into the multiple sub-bands by scattering according to the corresponding information embedding intensity to generate encrypted sub-band data.

3. The system according to claim 2, characterized in that, It also includes the introduction of dynamic normalization and dual-channel attention mechanisms during the scattering embedding process, wherein: The dynamic normalization adjusts the normalization center based on the sub-band mean and achieves a smooth distribution of the encrypted sensitive information in the sub-band through the normalization function; In the dual-channel attention mechanism, spatial attention is used to generate a position weight map to highlight important embedding regions, while channel attention is used to dynamically adjust the importance of different channels.

4. The system according to claim 3, characterized in that, The formula for dynamic normalization is: in, The output is the normalized result after dynamic normalization processing, where x is the original data to be processed in the subband, μ is the subband mean, k is an adjustable parameter, and ε is a minimum constant.

5. The system according to claim 1, characterized in that, The process by which the dual-channel processing architecture processes the spatial domain image to generate a dense carrier image includes: The visual fidelity channel uses generative adversarial networks to optimize image visual quality. The generator refines image details, and the discriminator ensures that the generated image is perceptually consistent with the natural image. The information embedding channel adopts a dynamic bit allocation mechanism, which allocates different embedding capacities according to the characteristics of image regions, allocating low embedding capacities to flat regions and high embedding capacities to regions with complex textures. Differential quantization encoding is performed on the processed frequency domain coefficients, including fine quantization strategy for low frequency coefficients and coarse quantization strategy for high frequency coefficients; A multi-objective loss function is used to jointly optimize the dual-channel processing. The loss function includes dynamic weight fusion loss, multi-scale structure preservation loss, and depth perception consistency loss. The dual-channel outputs are weighted and fused to generate a dense carrier image.

6. The system according to claim 5, characterized in that, The dynamic weight fusion loss is: The multi-scale structure preservation loss is: The depth-sensing consistency loss is: in, For dynamic weight fusion loss, To preserve loss in multi-scale structures, To deeply perceive consistency loss, For dynamic weighting coefficients, These are the pixel values ​​of the original image. Generate pixel values ​​for the image of the model. To generate the local mean of image X, The local mean of the original image Y. To generate the local standard deviation of image X, The local standard deviation of the original image Y. To generate the local covariance between image X and the original image Y, and As a preset constant, This is the feature extraction function.

7. The system according to claim 1, characterized in that, Receiving the encrypted carrier image and extracting the embedded encrypted sensitive information from the encrypted carrier image includes: The image containing the dense carrier is received and denoised using Wiener filtering. Color space conversion and inverse wavelet transform are performed on the filtered dense carrier image, and regional confidence analysis is performed on the channel sub-bands of the corresponding brightness components after reconstruction. For the first region where the regional confidence level is greater than the first set value, the encrypted sensitive information embedded in the first region is directly extracted according to the sub-band embedding rule; For the second region whose regional confidence is less than the second set value, a multi-head attention mechanism is used to associate context information to repair the information in the second region. After the repair is completed, the encrypted sensitive information embedded in the second region is extracted according to the sub-band embedding rule.

8. The system according to claim 1, characterized in that, Based on the checksum, block hash identifier, and metadata watermark embedded in the encrypted carrier image, a three-level integrity check is performed on the encrypted sensitive information, including: Calculate the CRC check value of the encrypted sensitive information, and compare the CRC check value with the check code embedded in the encrypted carrier image to determine the first-level check result; The image containing the dense carrier is divided into multiple image blocks according to the image features. The real-time hash value of each image block is calculated and compared with the block hash identifier embedded in the frequency domain coefficient of each image block. The second-level verification result is determined based on the number and distribution of image blocks that do not match. Explicit digital watermarks are extracted and verified from the metadata of the image containing the dense carrier, implicit digital watermarks are extracted and verified from the pixel data of the image containing the dense carrier, and the third-level verification result is determined based on the verification results of the two watermarks. When the first-level verification result, the second-level verification result, and the third-level verification result are all passed, the third-level integrity verification is determined to be successful.

9. The system according to claim 8, characterized in that, The tamper location based on block hash identifiers includes: The image is divided into image blocks of different sizes based on the texture features of the carrier image, and the hash value of each image block is calculated. The hash value is encoded and then embedded into the frequency domain coefficients of the corresponding block of the carrier image; During the extraction and verification process, the hash value of the image block is recalculated and compared with the extracted embedded hash value to locate the image block that has been tampered with.

10. The system according to claim 1, characterized in that, Also includes: If the verification fails, the information receiving end locates the tampered area based on the verification result of the three-level integrity verification, and repairs the tampered area using a generative repair network.

Images