A secure and efficient method for image data integrity verification

Abstract

The present invention discloses a safe and efficient method for image data integrity verification, which relates to the field of information security technology. It proposes a distributed oracle (BO) verification scheme based on BLS threshold signature, taking into account the integrity verification results of all oracle nodes. Only when it is recognized by more than half of the nodes can it pass the integrity verification, which greatly improves the reliability of the integrity verification results. In view of the privacy protection needs brought about by the value characteristics of image data itself, a two-stage privacy protection scheme is proposed, which only processes the image area containing privacy information rather than the entire image, effectively improving the efficiency of privacy protection and ensuring the privacy security of image data during storage. In view of the efficiency problems caused by the large-scale block and integration of data in existing schemes, a scheme is proposed to extract features from the entire image instead of blocking the image, which effectively improves the overall efficiency of the scheme.

Description

Technical Field

[0001] The present invention relates to the field of information security technology, and in particular to a safe and efficient image data integrity verification method. Background Art

[0002] In recent years, with the widespread adoption of smartphones and other mobile devices, more and more people are able to capture and share life's beautiful moments on their devices. This phenomenon has led to an explosive growth in the number of images, particularly on social media platforms, where the number of images uploaded each year is increasing exponentially at an astonishing rate. By 2023, the number of images stored on mobile devices is expected to exceed 9.9 trillion. These figures reflect a real challenge: with the massive amount of images generated daily, the pressure on local storage on mobile devices is increasing dramatically. To address this challenge, an increasing number of companies have launched cloud storage services, and the cloud service industry is showing positive development trends. These services not only alleviate the pressure on limited local storage space but also enable data access anytime, anywhere, driving the rapid development of cloud storage services. However, storing data on cloud servers also presents many challenges.

[0003] Due to the vast amount of stored data, cloud service providers (CSPs) may delete unused data for their own benefit to reduce storage costs. Furthermore, cloud servers may suffer data loss or corruption due to unexpected circumstances, and CSPs, out of self-interest, may attempt to cover up their mistakes by providing other data. Therefore, to effectively address the risks posed by dishonest CSPs, employing a range of integrity verification technologies is crucial for ensuring the security of cloud storage data.

[0004] Existing image data integrity verification technologies suffer from two drawbacks: an inability to effectively protect the privacy of image data; and low integrity verification efficiency and high computational overhead. Image data privacy protection refers to the process by which users upload their personal images to a CSP for storage. The CSP may then apply advanced data analysis algorithms to analyze the uploaded images to mine personal information and infer user behavior patterns and preferences, thereby providing personalized services and targeted advertising recommendations to increase profitability. This behavior can expose private information in images to unauthorized access or malicious exploitation by third parties, leading to risks of image leakage, illegal intrusion, and personal information misuse. Summary of the Invention

[0005] In order to solve the above problems, the present invention provides a safe and efficient image data integrity verification method, which ensures that the user's image data is securely stored in the cloud without leaking privacy, while also ensuring the efficiency of the integrity verification method.

[0006] To achieve the above objectives, the present invention provides a safe and efficient method for verifying the integrity of image data, which specifically includes the following steps:

[0007] Step S1: System initialization includes determining parameters, deploying smart contracts, and generating keys. The generated keys include the master key and user keys used for threshold signatures. The smart contract is used to publish the challenge and verification process on the blockchain.

[0008] Step S2: In the privacy protection stage, the private content in the image is identified and encrypted through detection based on the YOLOv8-MCA algorithm and fuzzification processing based on the Logistic Chaos Encryption Algorithm. The YOLOv8-MCA algorithm detects the location coordinates of the private content in the image, and the Logistic Chaos Encryption Algorithm fuzzifies the area containing the private content, making the private information unrecognizable.

[0009] Step S3: Feature processing stage, extracting feature information from the privacy-preserving image in step S2 through a homomorphic hash function, and uploading the feature information as image metadata to the blockchain for storage;

[0010] Step S4: The challenge and response verification phase is implemented jointly by the smart contract SC, the cloud service provider CSP and the distributed oracle BO, where BO periodically triggers SC to generate random challenges, CSP calculates and generates evidence based on the challenges, and BO verifies the integrity of the image data based on the evidence.

[0011] Preferably, in step S1, public parameters of bilinear mapping and elliptic curve are used to ensure the security and effectiveness of the key generation process;

[0012] Determine the public parameters required for parameter initialization threshold signature, the parameters include two orders The multiplicative cyclic group of ,in They are The generator of is a bilinear map: , It is a secure one-way hash function;

[0013] Deploy the contract, deploy the smart contract SC that generates random challenges, and the code of the smart contract is fully public on the blockchain BC;

[0014] Generate the key for threshold signature, including the following steps:

[0015] S11: Pick a random number As the master private key And calculate the master public key ;

[0016] S12: Random selection Order polynomial ,satisfy ,in , is the number of BOs;

[0017] S13: Oracle Apply for threshold key from the system, Private key for threshold signature , public key ;

[0018] Generating a user key specifically includes the following steps:

[0019] S1-1: Select an elliptic curve , select the base point ,satisfy The order is ;

[0020] S1-2: Select a pseudo-random number generation function and a homomorphic hash function ;

[0021] S1-3: Select As The private key of .

[0022] Preferably, in step S2, the YOLOv8-MCA algorithm in the privacy protection stage improves the detection accuracy of objects of different sizes and shapes by introducing a multi-scale axial attention mechanism. The training process of YOLOv8-MCA includes data preprocessing, model initialization, forward propagation, loss calculation, backpropagation, iterative training, model evaluation and parameter optimization.

[0023] The privacy protection stage includes the following steps:

[0024] S21: The user uploads the image to the CSP through the mobile terminal MT and uses the privacy protection scheme to protect the image before storing it. Privacy processing is obtained ;

[0025] S22: The processed image Upload to CSP for storage.

[0026] Preferably, in step S21, privacy protection includes two sub-stages. The first sub-stage determines whether the image contains private content based on the YOLOv8-MCA algorithm:

[0027] If the image contains private content, the location coordinates of the private content area are output and initiate the second sub-phase;

[0028] If there is no private content, the current stage ends directly;

[0029] In the second stage, the Logistic Chaos Encryption Algorithm will be used to Blurring the image area within the image, removing the blurring effect with the correct key, and restoring the original image. The specific formula is as follows:

[0030] ;

[0031] in is the current state, is the next state, It is the system parameter. The choice of system parameter determines the behavior pattern of the mapping output. The system parameter between 3.57 and 4.0 ensures the chaotic behavior of the system.

[0032] In image blurring applications, pixel blocks are rearranged or have their properties altered by iterating this mapping and applying the result, thus hiding information.

[0033] Preferably, in step S2, the image blurring process specifically includes the following steps:

[0034] S2-1: Determine the location information of the privacy area in the image After that, the area is divided into multiple color block units with a color of 32*32;

[0035] S2-2: A new chaotic mapping sequence is independently generated for the color block unit system. The generated sequence is highly unpredictable and is uniquely determined under given initial conditions.

[0036] S2-3: The color channels of the pixels within the color block are linearized into a one-dimensional array. Based on the chaotic sequences generated by each, the system performs a pixel reordering operation on the one-dimensional array to hide sensitive information in the image. The reordered pixel values ​​are represented by the following mapping relationship:

[0037] ;

[0038] in represents the pixel value in the original image, Represents the pixel value after blurring. It is a permutation function defined based on chaotic sequence;

[0039] S2-4: After pixel reordering is completed, all color blocks are reassembled according to their original positions to complete the blurring of the privacy area of ​​the entire image.

[0040] Preferably, in step S3, the following steps are specifically included:

[0041] S31: MT uses homomorphic hash functions Privacy-protected images Perform feature extraction to obtain feature information ;

[0042] The homomorphic hash function is of the form ;

[0043] in represents the numerical representation of the image data, is the selected base, is a large prime number;

[0044] For any two images and , whose hash value satisfies the properties shown in the following formula:

[0045] ;

[0046] ;

[0047] ;

[0048] S32: Feature Information After the extraction is complete, Upload to CSP storage.

[0049] Preferably, in step S4, the challenge and response verification phase is divided into a challenge generation phase , Proof Generation Phase , Verification phase and recording phase Four sub-stages, implemented jointly by SC, CSP and BO:

[0050] During the challenge generation phase, the aggregated BO periodically triggers the SC to generate challenges. , aggregate BO to obtain , after which each BO will Forward to CSP for inquiry;

[0051] In the proof generation phase, the CSP receives the challenge , CSP Respond and compute evidence based on stored image data ,in For the challenged image, is a random number, CSP will Send to all BOs;

[0052] During the verification phase, each BO middle Independently verify the evidence sent by the CSP ,The integrity verification result is negotiated by all BOs and is decided based on the ,principle of the minority obeys the majority;

[0053] During the recording phase, BC records the final integrity verification results in the following format: ,in is the verified image index set, The result of integrity verification.

[0054] Preferably, the specific steps of the challenge generation phase are as follows:

[0055] S41: BO scheduled task is performed by the timer control, Indicates the start time of the triggered task. Indicates the number of Trigger, The specific time point of the trigger Expressed as , at each time point , BO executes timing function Send a message to SC , this process is expressed as ;

[0056] S42: SC receives the message , select the number of challenged images For BC The maximum index value of the data, , according to the latest BC block Value Calculation , , constituting a challenge set ,in is the index set of challenged images, is a set of random numbers;

[0057] S43: Yes , query feature information BC will challenge and Recorded in the log in the form of ;

[0058] S44: BO monitors BC to obtain log information , respectively, will challenge Forwarded to CSP.

[0059] Preferably, the verification phase is implemented based on BLS threshold signatures, and the specific steps are as follows:

[0060] S4-1: BO receives evidence sent by CSP , first calculate ;

[0061] S4-2: BO based on the query in the blockchain ,calculate ;

[0062] S4-3: Has homomorphic properties , BO verification and Are they equal?

[0063] If the equation holds, output the result for , indicating that the image data is completely stored in the CSP and has not been tampered with;

[0064] If the equation does not hold, the verification fails and the result is output. for , indicating that the image data has been damaged in the CSP;

[0065] S4-4: If CSP passes BO verification, BO uses its own threshold signature private key right Calculate the signature And through the secure channel Return to the main BO, and temporarily stored by the BO ;

[0066] S4-5: Master BO receives BO returned After that, the complete signature is aggregated , The calculation method is shown in the formula:

[0067] ;

[0068] S4-6: The main BO is aggregated Later General Sent to BC, BC verifies whether the following formula is true Correctness:

[0069] ;

[0070] If the verification passes, this round of integrity verification is successful.

[0071] Preferably, a UMHT index structure is used to improve data retrieval efficiency. The UMHT has a two-layer index structure. The first layer is instantiated as a User array, and the second layer points to a Merkle tree MHT established for each User. The MHT is a tree data structure that constructs a binary tree structure by recursively hashing the original data block.

[0072] exist After insertion When inserting a node, it is implemented through the following steps:

[0073] Step 1: Calculation Building a new node ;

[0074] Step 2: Find in MHT , build a new node and will replace location, With the new node becomes 's child nodes;

[0075] Step 3: New Node After insertion, update the hash values ​​of all parent nodes on the node path in the MHT;

[0076] Step 4: MHT root node update completed, new data block Successfully inserted into the MHT;

[0077] Deleting a Node , do the following:

[0078] (1) Found in MHT and its sibling nodes ;

[0079] (2) Use replace and The parent node and delete ;

[0080] (3) From Start recursively updating the hash values ​​of all parent nodes until the root node of the MHT.

[0081] Therefore, the present invention adopts the above-mentioned safe and efficient image data integrity verification method, which has the following beneficial effects:

[0082] (1) This paper proposes a distributed oracle (BO) verification scheme based on BLS threshold signature. Taking into account the integrity verification results of all oracle nodes, only when it is recognized by more than half of the nodes can it pass the integrity verification, which greatly improves the reliability of the integrity verification results.

[0083] (2) In response to the privacy protection needs brought about by the value characteristics of image data itself, the present invention proposes a two-stage privacy protection scheme, which only processes the image area containing privacy information rather than the entire image, effectively improving the efficiency of privacy protection and ensuring the privacy security of image data during the storage process.

[0084] (3) In order to solve the efficiency problems caused by the large-scale segmentation and integration of data in existing solutions, the present invention proposes a solution to extract features from the entire image instead of segmenting the image, which effectively improves the overall efficiency of the solution.

[0085] (4) To address the problem of slow data query speed in integrity verification schemes, the present invention proposes a data structure based on secondary index, the User Merkel Hash Tree (UMHT). The primary index of the UMHT is initialized as a user array, where each user's array element points to a Merkel tree established for the user, which improves data retrieval efficiency while supporting efficient dynamic operations on data.

[0086] (5) The present invention utilizes the transparency, immutability and decentralization of blockchain to provide a public and permanent audit trail for each verification, thereby enhancing the transparency and credibility of the system as a whole.

[0087] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 This is a system model diagram of a safe and efficient image data integrity verification method of the present invention;

[0089] Figure 2 Flowchart of the secure and traceable data transmission method of the present invention;

[0090] Figure 3 It is the training flow chart of YOLOv8-MCA in the present invention;

[0091] Figure 4 It is a flow chart of the image blurring and restoration process in the present invention;

[0092] Figure 5 This is a comparison chart of the time expenditure in the Channel stage of the comparative example of the present invention;

[0093] Figure 6This is a comparison chart of the time overhead in the Genproof stage of the comparative example of the present invention;

[0094] Figure 7 This is an overall flow chart of a safe and efficient image data integrity verification method of the present invention. DETAILED DESCRIPTION

[0095] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0096] Unless otherwise defined, technical or scientific terms used in the present invention shall have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

[0097] The words “include” or “comprising” and similar words used in the present invention mean that the elements before the word include the elements listed after the word, and do not exclude the possibility of also including other elements. The orientation or position relationship indicated by the terms “inside”, “outside”, “upper”, “lower”, etc. is based on the orientation or position relationship shown in the accompanying drawings. It is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation of the present invention. When the absolute position of the described object changes, the relative position relationship may also change accordingly. In the present invention, unless otherwise clearly stipulated and limited, the terms such as “attachment” should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral whole; it can be directly connected or indirectly connected through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances.

[0098] like Figure 1 As shown, a safe and efficient method for verifying the integrity of image data includes the following steps:

[0099] Step S1: System initialization includes determining parameters, deploying smart contracts, and generating keys. The generated keys include the master key and user keys used for threshold signatures. The smart contract is used to publish the challenge and verification process on the blockchain.

[0100] In step S1, the public parameters of bilinear mapping and elliptic curve are used to ensure the security and effectiveness of the key generation process;

[0101] Determine the public parameters required for parameter initialization threshold signature, the parameters include two orders The multiplicative cyclic group of ,in They are The generator of is a bilinear map: , It is a secure one-way hash function;

[0102] Deploy the contract, deploy the smart contract SC that generates random challenges, and the code of the smart contract is fully public on the blockchain BC;

[0103] Generate the key for threshold signature, including the following steps:

[0104] S11: Pick a random number As the master private key And calculate the master public key ;

[0105] S12: Random selection Order polynomial ,satisfy ,in , is the number of BOs;

[0106] S13: Oracle Apply for threshold key from the system, Private key for threshold signature , public key ;

[0107] Generating a user key specifically includes the following steps:

[0108] S1-1: Select an elliptic curve , select the base point ,satisfy The order is ;

[0109] S1-2: Select a pseudo-random number generation function and a homomorphic hash function ;

[0110] S1-3: Select As The private key of .

[0111] Step S2: In the privacy protection stage, the private content in the image is identified and encrypted through detection based on the YOLOv8-MCA algorithm and fuzzification processing based on the Logistic Chaos Encryption Algorithm. The YOLOv8-MCA algorithm detects the location coordinates of the private content in the image, and the Logistic Chaos Encryption Algorithm fuzzifies the area containing the private content, making the private information unrecognizable.

[0112] In step S2, the YOLOv8-MCA algorithm in the privacy protection stage improves the detection accuracy of objects of different sizes and shapes by introducing a multi-scale axial attention mechanism. The training process of YOLOv8-MCA includes data preprocessing, model initialization, forward propagation, loss calculation, backpropagation, iterative training, model evaluation and parameter optimization.

[0113] The privacy protection stage includes the following steps:

[0114] S21: The user uploads the image to the CSP through the mobile terminal MT and uses the privacy protection scheme to protect the image before storing it. Privacy processing is obtained ;

[0115] S22: The processed image Upload to CSP for storage.

[0116] In step S21, privacy protection includes two sub-stages. The first sub-stage determines whether the image contains private content based on the YOLOv8-MCA algorithm:

[0117] If the image contains private content, the location coordinates of the private content area are output and initiate the second sub-phase;

[0118] If there is no private content, the current stage ends directly;

[0119] In the second stage, the Logistic Chaos Encryption Algorithm will be used to Blurring the image area within the image, removing the blurring effect with the correct key, and restoring the original image. The specific formula is as follows:

[0120] ;

[0121] in is the current state, , is the next state, It is the system parameter. The choice of system parameter determines the behavior pattern of the mapping output. The system parameter between 3.57 and 4.0 ensures the chaotic behavior of the system.

[0122] In image blurring applications, pixel blocks are rearranged or have their properties altered by iterating this mapping and applying the result, thus hiding information.

[0123] In step S2, the image blurring process specifically includes the following steps:

[0124] S2-1: Determine the location information of the privacy area in the image After that, the area is divided into multiple color block units with a color of 32*32;

[0125] S2-2: A new chaotic mapping sequence is independently generated for the color block unit system. The generated sequence is highly unpredictable and is uniquely determined under given initial conditions.

[0126] S2-3: The color channels of the pixels within the color block are linearized into a one-dimensional array. Based on the chaotic sequences generated by each, the system performs a pixel reordering operation on the one-dimensional array to hide sensitive information in the image. The reordered pixel values ​​are represented by the following mapping relationship:

[0127] ;

[0128] in represents the pixel value in the original image, Represents the pixel value after blurring. It is a permutation function defined based on chaotic sequence;

[0129] S2-4: After pixel reordering is completed, all color blocks are reassembled according to their original positions to complete the blurring of the privacy area of ​​the entire image.

[0130] Step S3: Feature processing stage, extracting feature information from the privacy-preserving image in step S2 through a homomorphic hash function, and uploading the feature information as image metadata to the blockchain for storage;

[0131] In step S3, the following steps are specifically included:

[0132] S31: MT uses homomorphic hash functions Privacy-protected images Perform feature extraction to obtain feature information ;

[0133] The homomorphic hash function is of the form ;

[0134] in represents the numerical representation of the image data, is the selected base, is a large prime number;

[0135] For any two images and , whose hash value satisfies the properties shown in the following formula:

[0136] ;

[0137] ;

[0138] ;

[0139] S32: Feature Information After the extraction is complete, Upload to CSP storage.

[0140] Step S4: The challenge and response verification phase is implemented jointly by the smart contract SC, the cloud service provider CSP and the distributed oracle BO, where BO periodically triggers SC to generate random challenges, CSP calculates and generates evidence based on the challenges, and BO verifies the integrity of the image data based on the evidence.

[0141] In step S4, the challenge and response verification phase is divided into the challenge generation phase , Proof Generation Phase , Verification phase and recording phase Four sub-stages, implemented jointly by SC, CSP and BO:

[0142] During the challenge generation phase, the aggregated BO periodically triggers the SC to generate challenges. , aggregate BO to obtain , after which each BO will Forward to CSP for inquiry;

[0143] In the proof generation phase, the CSP receives the challenge , CSP Respond and compute evidence based on stored image data ,in For the challenged image, is a random number, CSP will Send to all BOs;

[0144] During the verification phase, each BO middle Independently verify the evidence sent by the CSP ,The integrity verification result is negotiated by all BOs and is decided based on the ,principle of the minority obeys the majority;

[0145] During the recording phase, BC records the final integrity verification results in the following format: ,in is the verified image index set, The result of integrity verification.

[0146] The specific steps of the challenge generation phase are as follows:

[0147] S41: BO scheduled task is performed by the timer control, Indicates the start time of the triggered task. Indicates the number of Trigger, The specific time point of the trigger Expressed as , at each time point , BO executes timing function Send a message to SC , this process is expressed as ;

[0148] S42: SC receives the message , select the number of challenged images For BC The maximum index value of the data, , according to the latest BC block Value Calculation , , constituting a challenge set ,in is the index set of challenged images, is a set of random numbers;

[0149] S43: Yes , query feature information BC will challenge and Recorded in the log in the form of ;

[0150] S44: BO monitors BC to obtain log information , respectively, will challenge Forwarded to CSP.

[0151] The verification phase is implemented based on BLS threshold signatures. The specific steps are as follows:

[0152] S4-1: BO receives evidence sent by CSP , first calculate ;

[0153] S4-2: BO based on the query in the blockchain ,calculate ;

[0154] S4-3: Has homomorphic properties , BO verification and Are they equal?

[0155] If the equation holds, output the result for , indicating that the image data is completely stored in the CSP and has not been tampered with;

[0156] If the equation does not hold, the verification fails and the result is output. for , indicating that the image data has been damaged in the CSP;

[0157] S4-4: If CSP passes BO verification, BO uses its own threshold signature private key right Calculate the signature And through the secure channel Return to the main BO, and temporarily stored by the BO ;

[0158] S4-5: Master BO receives BO returned After that, the complete signature is aggregated , The calculation method is shown in the formula:

[0159] ;

[0160] S4-6: The main BO is aggregated Later General Sent to BC, BC verifies whether the following formula is true Correctness:

[0161] ;

[0162] If the verification passes, this round of integrity verification is successful.

[0163] The UMHT index structure is used to improve data retrieval efficiency. The UMHT has a two-layer index structure. The first layer is instantiated as a User array, and the second layer points to a Merkle tree (MHT) established for each User. The MHT is a tree data structure that constructs a binary tree structure by recursively hashing the original data block.

[0164] exist After insertion When inserting a node, it is implemented through the following steps:

[0165] Step 1: Calculation Building a new node ;

[0166] Step 2: Find in MHT , build a new node and will replace location, With the new node becomes 's child nodes;

[0167] Step 3: New Node After insertion, update the hash values ​​of all parent nodes on the node path in the MHT;

[0168] Step 4: MHT root node update completed, new data block Successfully inserted into the MHT;

[0169] Deleting a Node , do the following:

[0170] (1) Found in MHT and its sibling nodes ;

[0171] (2) Use replace and The parent node and delete ;

[0172] (3) From Start recursively updating the hash values ​​of all parent nodes until the root node of the MHT.

[0173] Example

[0174] This embodiment is used to detect whether the image data cloud server is complete, and includes four entities:

[0175] Mobile Terminals (MTs): These devices, such as smartphones and tablets, serve as the source of image uploads. Given the relatively limited storage capacity of these devices, users typically upload images to a CSP to free up local storage space. During the upload process, the mobile terminal performs a series of preprocessing tasks, such as privacy protection and image feature extraction, to optimize storage efficiency while protecting user data privacy.

[0176] Cloud Service Provider (CSP): A CSP is a cloud computing platform with powerful storage and computing resources, primarily responsible for providing data storage services. As a semi-honest entity, the CSP responds to queries from blockchain smart contracts, computes the evidence required for integrity verification, and returns the evidence to the BO. However, for its own benefit, the CSP may spy on and mine user privacy information, concealing data loss or corruption from users. The CSP's responsibilities in the system are primarily two-fold: acting as a data storage provider and computing evidence to respond to integrity verification queries.

[0177] Blockchain Oracle (BO): Serving as a critical bridge between BC and CSP, the aggregated oracle regularly triggers blockchain smart contracts through scheduled tasks, generating integrity verification challenges for image data stored in the CSP. Aggregated oracles are regularly elected and replaced through the oracle network. Each oracle node independently receives evidence from the CSP and compares it with the image feature information stored in the blockchain. All oracle nodes collectively determine the final integrity verification result through a decentralized consensus mechanism based on BLS threshold signatures. Once consensus is reached, the verification result is uploaded to the blockchain for public display. This distributed verification method not only improves the transparency and credibility of the verification process but also enhances the system's resistance to single points of failure and malicious attacks.

[0178] Blockchain (BC): BC plays a crucial role in the system. First, it stores feature information extracted before image upload, providing the foundational data for integrity verification. When a scheduled task (called a Blockchain Object Manager (BOM)) is triggered, the blockchain smart contract generates an integrity verification challenge and forwards it to the CSP through the BOM. During the integrity verification process, each BOM compares the evidence provided by the CSP with the image feature information on the blockchain. The verification results are then recorded and stored on the blockchain, providing an open, transparent, and immutable record of the verification results for the entire system, thereby ensuring system integrity and transparency.

[0179] Define two orders as The multiplicative cyclic group of ,in They are The generator of Take 256 bits, and p takes a 256-bit random prime number). and are all secure collision-resistant one-way hash functions, where (Elliptic curve hash function required in BLS signature), h uses SHA-256 secure hash function. Select a random number As the master private key And calculate the master public key The number of BOs N is 100. =51. Then Private key for threshold signature , public key .

[0180] Assume that there is a MT in the system, which performs privacy protection on the uploaded image set I. Assume that set I contains 100 data blocks. MT first uses the YOLOv8-MCA algorithm to detect whether the image contains private content. If the image contains private content, it outputs the location coordinates of the private content area. And start the second sub-stage, MT uses the Logistic Chaos Encryption Algorithm to further process the identified target area to achieve a fuzzy effect. After the privacy protection process, 100 new blocks can be obtained, which are recorded as .

[0181] MT uses homomorphic hash functions Privacy-protected images Perform feature extraction to obtain feature information After feature information extraction is completed, Upload to CSP storage. , used to extract feature information from privacy-protected images, where Represents the numerical representation of image data, here we take is 1024 bits, and the base Also take 1024 bits.

[0182] BO triggers the smart contract at a certain time point t, and the smart contract generates the result After receiving the message, SC selects 460 challenge images. , according to the latest BC block Value Calculation , , which ultimately constitutes the challenge set After BO obtains Chal, it sends it to CSP. CSP Respond and compute evidence based on stored image data , then BO performs the integrity verification algorithm to output the results.

[0183] Simulation tests were conducted using the Go language, using the crypto library to implement some cryptographic operations. Elliptic curve functions were constructed using BIST P256, and HMAC-SHA256 was used to implement HMAC(). Both of these functions use 256-bit cryptography, corresponding to the 3072-bit RSA cryptography. The simulations were conducted on Ubuntu 16.04.12 64-bit, an Intel i7-8750H (2.5GHz) CPU, and 24GB of RAM.

[0184] Comparative Example

[0185] In order to verify the effectiveness of the secure and efficient image data auditing scheme proposed in this paper, the computational overhead of this scheme is simulated and compared with the computational overhead of two similar schemes, PATHAT and BCM. Figure 5 Figure 6 shown.

[0186] exist Figure 5 In the challenge data block, the challenge data block is increased from 50 to 500. For the difference of challenge data blocks, the time cost of the challenge phase of the present invention is compared with that of the other two schemes. In this step, our scheme has a clear computational advantage. Compared to the BCM scheme, our time overhead increases linearly with the number of challenge blocks, and the rate of increase is significantly lower than that of the BCM scheme. Compared to the PATHAT scheme, although the growth rate of our scheme is roughly the same, the time overhead is significantly lower than that of the PATHAT scheme at all challenge block numbers. This demonstrates excellent performance.

[0187] exist Figure 6 In the comparison, the settings are Figure 5 The experimental results show that This scheme has a significant computational advantage over PATHAT and BCM in these steps. Furthermore, its time overhead increases linearly with the number of challenge blocks, significantly slower than those of PATHAT and BCM. When the number of challenge blocks reaches 500, this scheme's time overhead is approximately 100ms lower than PATHAT, demonstrating its high performance even with large-scale integrity challenges.

[0188] in conclusion

[0189] When the adversary attempts to launch a replacement attack, this scheme can resist the replacement attack due to the anti-collision property of the hash function and the negligible probability of cracking the random function.

[0190] When the adversary attempts to launch a forgery attack, since the probability of cracking the random function can be ignored and the probability of solving the CDH problem can be ignored, this scheme can resist the forgery attack.

[0191] When an adversary attempts to launch a substitution attack, the adversary does not know the data block or the private key used to sign the user. Assuming the probability of the signature algorithm being cracked is negligible, the adversary cannot pass the verification. Therefore, this scheme can resist substitution attacks.

[0192] Therefore, the present invention adopts the above-mentioned safe and efficient image data integrity verification method, introduces a distributed oracle verification scheme based on BLS threshold signature, improves the reliability of the verification results, and through a two-stage privacy protection strategy, only processes the image area containing private information, thereby effectively improving the privacy protection efficiency and ensuring the privacy security of image data stored in the cloud; proposes a scheme for extracting the overall features of the image without blocking, optimizes the processing efficiency, and accelerates the data query speed through the user Merkle hash tree structure, supporting efficient dynamic operations.

[0193] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A secure and efficient method for verifying the integrity of image data, characterized by: The specific steps include: Step S1: System initialization includes determining parameters, deploying smart contracts, and generating keys. The generated keys include the master key and user keys used for threshold signatures. The smart contract is used to publish the challenge and verification process on the blockchain. Step S2: In the privacy protection stage, the private content in the image is identified and encrypted through detection based on the YOLOv8-MCA algorithm and fuzzification processing based on the Logistic Chaos Encryption Algorithm. The YOLOv8-MCA algorithm detects the location coordinates of the private content in the image, and the Logistic Chaos Encryption Algorithm fuzzifies the area containing the private content, making the private information unrecognizable. Step S3: Feature processing stage, extracting feature information from the privacy-preserving image in step S2 through a homomorphic hash function, and uploading the feature information as image metadata to the blockchain for storage; Step S4: Challenge and response verification phase, implemented by the smart contract SC, cloud service provider CSP and distributed oracle BO. BO periodically triggers SC to generate random challenges. CSP calculates and generates evidence based on the challenges. BO verifies the integrity of the image data based on the evidence. In step S4, the challenge and response verification phase is divided into the challenge generation phase , Proof Generation Phase , Verification phase and recording phase Four sub-stages, implemented jointly by SC, CSP and BO: During the challenge generation phase, the aggregated BO periodically triggers the SC to generate challenges. , aggregate BO to obtain , after which each BO will Forward to CSP for inquiry; In the proof generation phase, the CSP receives the challenge , CSP Respond and compute evidence based on stored image data ,in For the challenged image, is a random number, CSP will Send to all BOs; During the verification phase, each BO middle Independently verify the evidence sent by the CSP ,The integrity verification result is negotiated by all BOs and is decided based on the ,principle of the minority obeys the majority; The verification phase is implemented based on BLS threshold signatures. The specific steps are as follows: S4-1: BO receives evidence sent by CSP , first calculate ; S4-2: BO based on the query in the blockchain ,calculate ; S4-3: Has homomorphic properties , BO verification and Are they equal? If the equation holds, output the result for , indicating that the image data is completely stored in the CSP and has not been tampered with; If the equation does not hold, the verification fails and the result is output. for , indicating that the image data has been damaged in the CSP; S4-4: If CSP passes BO verification, BO uses its own threshold signature private key right Calculate the signature And through the secure channel Return to the main BO, and temporarily stored by the BO ; S4-5: Master BO receives BO returned After that, the complete signature is aggregated , The calculation method is shown in the formula: ； S4-6: The main BO is aggregated Later General Sent to BC, BC verifies whether the following formula is true Correctness: ； If the verification passes, the integrity verification of this round is successful; During the recording phase, BC records the final integrity verification results in the following format: ,in is the verified image index set, The result of integrity verification.

2. A secure and efficient method for verifying image data integrity according to claim 1, characterized in that: In step S1, the public parameters of bilinear mapping and elliptic curve are used to ensure the security and effectiveness of the key generation process; Determine the public parameters required for parameter initialization threshold signature, the parameters include two orders The multiplicative cyclic group of ,in They are The generator of is a bilinear map: , It is a secure one-way hash function; Deploy the contract, deploy the smart contract SC that generates random challenges, and the code of the smart contract is fully public on the blockchain BC; Generate a key for threshold signature, including the following steps: S11: Pick a random number As the master private key And calculate the master public key ; S12: Random selection Order polynomial ,satisfy ,in , is the number of BOs; S13: Oracle Apply for threshold key from the system, Private key for threshold signature , public key ; Generating a user key specifically includes the following steps: S1-1: Select an elliptic curve , select the base point ,satisfy The order is ; S1-2: Select a pseudo-random number generation function and a homomorphic hash function ; S1-3: Select As The private key of .

3. A secure and efficient method for verifying image data integrity according to claim 1, characterized in that: In step S2, the YOLOv8-MCA algorithm in the privacy protection stage improves the detection accuracy of objects of different sizes and shapes by introducing a multi-scale axial attention mechanism. The training process of YOLOv8-MCA includes data preprocessing, model initialization, forward propagation, loss calculation, backpropagation, iterative training, model evaluation and parameter optimization. The privacy protection stage includes the following steps: S21: The user uploads the image to the CSP through the mobile terminal MT and uses the privacy protection scheme to protect the image before storing it. Privacy processing is obtained ; S22: The processed image Upload to CSP for storage.

4. A safe and efficient method for verifying image data integrity according to claim 3, characterized in that: In step S21, privacy protection includes two sub-stages. The first sub-stage determines whether the image contains private content based on the YOLOv8-MCA algorithm: If the image contains private content, the location coordinates of the private content area are output and initiate the second sub-phase; If there is no private content, the current stage ends directly; In the second stage, the Logistic Chaos Encryption Algorithm will be used to Blurring the image area within the image, removing the blurring effect with the correct key, and restoring the original image. The specific formula is as follows: ； in is the current state, 0 1, is the next state, It is the system parameter. The choice of system parameter determines the behavior pattern of the mapping output. The system parameter between 3.57 and 4.0 ensures the chaotic behavior of the system. In image blurring applications, pixel blocks are rearranged or have their properties altered by iterating this mapping and applying the result to hide information.

5. A safe and efficient method for verifying image data integrity according to claim 4, characterized in that: In step S2, the image blurring process specifically includes the following steps: S2-1: Determine the location information of the privacy area in the image Then, the area is divided into multiple pigments. Color block unit; S2-2: A new chaotic mapping sequence is independently generated for the color block unit system. The generated sequence is highly unpredictable and is uniquely determined under given initial conditions. S2-3: The color channels of the pixels within the color block are linearized into a one-dimensional array. Based on the chaotic sequences generated by each, the system performs a pixel reordering operation on the one-dimensional array to hide sensitive information in the image. The reordered pixel values ​​are represented by the following mapping relationship: ； in represents the pixel value in the original image, Represents the pixel value after blurring. It is a permutation function defined based on chaotic sequence; S2-4: After pixel reordering is completed, all color blocks are reassembled according to their original positions to complete the blurring of the privacy area of ​​the entire image.

6. A safe and efficient method for verifying image data integrity according to claim 5, characterized in that: In step S3, the following steps are specifically included: S31: MT uses homomorphic hash functions Privacy-protected images Perform feature extraction to obtain feature information ; The homomorphic hash function is of the form ; in represents the numerical representation of the image data, is the selected base, is a large prime number; For any two images and , whose hash value satisfies the properties shown in the following formula: ； ； ； S32: Feature Information After the extraction is complete, Upload to CSP storage.

7. A safe and efficient method for verifying image data integrity according to claim 6, characterized in that: The specific steps of the challenge generation phase are as follows: S41: BO scheduled task is performed by the timer control, Indicates the start time of the triggered task. Indicates the number of Trigger, The specific time point of the trigger Expressed as , at each time point , BO executes timing function Send a message to SC , this process is expressed as ; S42: SC receives the message , select the number of challenged images For BC The maximum index value of the data, , according to the latest BC block Value Calculation , , constituting a challenge set ,in is the index set of challenged images, is a set of random numbers; S43: Yes , query feature information BC will challenge and Recorded in the log in the form of ; S44: BO monitors BC to obtain log information , respectively, will challenge Forwarded to CSP.

8. A safe and efficient image data integrity verification method according to claim 1, characterized in that: The UMHT index structure is used to improve data retrieval efficiency. The UMHT has a two-layer index structure. The first layer is instantiated as a User array, and the second layer points to a Merkle tree (MHT) established for each User. The MHT is a tree data structure that constructs a binary tree structure by recursively hashing the original data block. exist After insertion When inserting a node, it is implemented through the following steps: Step 1: Calculation Building a new node ; Step 2: Find in MHT , build a new node and will replace location, With the new node becomes 's child nodes; Step 3: New Node After insertion, update the hash values ​​of all parent nodes on the node path in the MHT; Step 4: MHT root node update completed, new data block Successfully inserted into the MHT; Deleting a Node , do the following: (1) Found in MHT and its sibling nodes ; (2) Use replace and The parent node of and delete ; (3) From Start recursively updating the hash values ​​of all parent nodes until the root node of the MHT.

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