Digital artwork block chain authentication and traceability method and system

Abstract

The invention relates to the field of digital artwork management equipment, in particular to a digital artwork block chain authentication and traceability method and system, and the system comprises a dynamic data capture engine module which comprises an entropy monitoring unit and a self-adaptive framing controller; the chain type evidence storage processing module is connected with the dynamic data capture engine module; the cross-link routing collaboration module is connected with the chain type evidence storage processing module; and the dynamic traceability execution module is connected with the cross-link routing collaboration module. The problems that in an existing digital artwork authentication technology, dynamic content full-life-cycle tracking is missing, multi-modal data cross-chain cooperation efficiency is low, and privacy protection and verification are contradictory are solved, continuous state changes of dynamic artworks are converted into irreversible light-weight Hash links through the self-adaptive framing compression and chained incremental Hash technology, and the privacy protection and verification efficiency of the dynamic artworks is improved. And the technical barrier that privacy disclosure and copyright traceability are difficult to consider in a dynamic artwork commercialization scene is overcome.

Description

Technical Field

[0001] The present invention relates to the field of digital artwork management equipment, and in particular to a method and system for blockchain authentication and traceability of digital artworks. Background Art

[0002] With the rapid development of digital artworks, such as NFTs, AI-generated art, and dynamic interactive works in the metaverse, the requirements for authentication and traceability are becoming increasingly complex. Traditional blockchain technology primarily targets static digital assets, such as fixed images and text, providing ownership authentication and transaction record storage. However, it has significant technical flaws in the following scenarios:

[0003] Insufficient traceability of dynamic content: Existing blockchain systems only record the initial hash and transaction history of digital artworks and are unable to track the state changes of dynamic works throughout their lifecycle. These dynamic changes are not recorded on-chain, resulting in a lack of authenticity verification of the subsequent state of the work, posing the risk of tampering or copyright disputes.

[0004] Inefficient multimodal data fusion and verification: Data of different modalities are stored in a decentralized manner, lacking a structured association mechanism. Verifying the integrity of multimodal data requires multiple queries across off-chain systems, which is inefficient and vulnerable to attacks.

[0005] Bottlenecks in cross-chain real-time collaborative authentication: A single blockchain cannot support high-concurrency, real-time status updates of dynamic digital artworks. Existing cross-chain technologies have the following limitations: Synchronization delay: Cross-chain status synchronization relies on periodic verification and cannot meet millisecond-level real-time authentication requirements; Resource waste: Cross-chain transmission of full data causes a surge in network load, making it unsuitable for lightweight, dynamic, incremental update scenarios;

[0006] The contradiction between the immutability of dynamic content and privacy: Some dynamic digital artworks need to hide sensitive information during on-chain verification, but existing zero-knowledge proof schemes are not designed and optimized for multimodal dynamic data, making it difficult to strike a balance between verification efficiency and privacy protection. Summary of the Invention

[0007] The present invention provides a method and system for blockchain authentication and traceability of digital artworks. The technical problems to be solved are: This solution solves the three core problems in existing digital artwork authentication technologies: the lack of full life cycle tracking of dynamic content, the low efficiency of cross-chain collaboration of multimodal data, and the contradiction between privacy protection and verification. Through adaptive frame compression and chain incremental hashing technology, the continuous state changes of dynamic artworks are converted into irreversible lightweight hash links, breaking through the limitation of traditional static evidence that cannot trace version iterations; with the help of lightweight cross-chain routing protocol and layered state synchronization engine, efficient verification and millisecond-level consensus synchronization of multimodal data between heterogeneous blockchains are achieved, solving the bottlenecks of data silos and cross-chain delays; at the same time, verifiable privacy tags and homomorphic desensitization mechanisms are introduced to ensure the auditability of full-link data under the premise of encrypting sensitive information, overcoming the technical obstacles that are difficult to balance privacy leakage and copyright traceability in the commercialization scenario of dynamic artworks.

[0008] In order to achieve the above-mentioned purpose of the invention, the method for blockchain authentication and traceability of digital artworks of the present invention comprises the following steps: Step S100: Real-time capture of multimodal dynamic data streams of dynamic digital artworks, wherein the multimodal dynamic data streams include user uploaded image data streams, user interactive behavior data streams, embedded sensor trigger data streams, and AI algorithm self-iteration parameter streams, wherein the user uploaded image data streams are used to extract clothing feature vectors through convolutional neural networks. , and based on the threshold condition Triggering compliance audits;

[0009] Step S200: Adaptive frame compression is performed on the fused multimodal dynamic data stream to generate a continuous state frame sequence with timestamp-version dual indexes. An irreversible state link is generated through a chained incremental hash algorithm. Digital artwork synthesis based on a generative adversarial network is performed on the user-uploaded image data stream:

[0010]

[0011] in Positioning heatmaps for artworks, is an image encoder;

[0012] Step S300: Based on the Verifiable Data Object Protocol, the state frame sequence is bound to the meta-features of the multimodal dynamic data stream to construct a structured authentication object including the main chain evidence anchor, cross-chain routing tags, and traceable data fingerprints;

[0013] Step S400: Write the main chain evidence anchor point into the main chain smart contract, and distribute the structured authentication object to the corresponding heterogeneous side chain node group through the lightweight cross-chain routing protocol, triggering multi-chain parallel consensus verification;

[0014] Step S500: After the main chain and the side chain group are verified, the verification results are written back to the main chain evidence anchor point based on the hierarchical state synchronization engine, generating a dynamic traceability certificate including a cross-chain time lock and a multi-chain hash chain;

[0015] Step S600: When responding to a traceability request, parse the dynamic traceability certificate and activate the multi-chain collaborative query agent to extract the full life cycle data topology of the embedded state frame sequence from the main chain and side chain group. The topology includes all hash link nodes in the digital artwork generation process.

[0016] Furthermore, the step S200 includes:

[0017] Step S210: Receive multimodal dynamic data stream and calculate its information entropy in real time , and dynamically adjust the framing threshold based on the entropy value ,in is a preset nonlinear function;

[0018] Step S220: When the cumulative change of the data stream satisfy When the frame operation is triggered to generate the status frame , and the framing logic is dynamically configured by the main chain smart contract through the oracle;

[0019] Step S230: For each state frame Perform chained incremental hashing to generate hash links ,in It is a threshold-based homomorphic compression algorithm;

[0020] Step S240: Output hash link and timestamp-version tag To the structured authentication object encapsulation process.

[0021] Furthermore, the step S300 includes: step S310: extracting an interaction event feature vector from the user interaction behavior data stream, and encoding the feature vector into a standardized event identifier;

[0022] Step S320: performing differential coding compression on the AI ​​algorithm self-iteration parameter stream to generate a lightweight parameter update package;

[0023] Step S330: performing spatiotemporal association binding on the standardized event identifier, the lightweight parameter update package, and the hash link to generate a multimodal association metadata block;

[0024] Step S340: Generate a main chain evidence anchor point based on the main chain smart contract address, and generate a cross-chain routing tag based on the side chain node topology structure to complete the encapsulation of the structured authentication object.

[0025] Furthermore, the step S400 includes: step S410: verifying the format compliance of the structured authentication object through the main chain smart contract, and triggering a cross-chain rollback instruction if the verification fails;

[0026] Step S420: When the verification passes, the cross-chain routing label is decomposed into multiple sidechain shard routing instructions, and the structured authentication object is distributed to the target sidechain node group;

[0027] Step S430: Using a zero-knowledge proof algorithm in the side chain node group to generate a compressed verification credential for the multimodal associated metadata block, and submitting the compressed verification credential to the side chain shard consensus network.

[0028] Furthermore, the step S500 includes: step S510: the main chain node monitors the verification results of the side chain shard consensus network, and when more than a preset proportion of the side chain shards pass the verification, the cross-chain time lock protocol is triggered;

[0029] Step S520: During the validity period of the cross-chain time lock protocol, the verification hash of the side chain shard is written back to the main chain evidence anchor point to generate a multi-chain hash chain;

[0030] Step S530: If the cross-chain time lock protocol times out and no consensus is reached, the cross-chain arbitration contract is activated to reallocate the verification task.

[0031] Furthermore, step S300 also includes: step S350: performing selective homomorphic desensitization processing on sensitive fields in the user interaction behavior data stream to generate a verifiable privacy label ,in is a random salt value;

[0032] Step S360: embed the verifiable privacy tag into the structured authentication object and declare the privacy verification policy in the dynamic traceability credential.

[0033] Furthermore, the step S600 includes:

[0034] Step S610: Parse the multi-chain hash chain in the dynamic traceability certificate and obtain the initial version hash and ownership information from the main chain;

[0035] Step S620: Locate the side chain shard node according to the cross-link routing label, and pull the incremental state frame sequence and associated metadata block;

[0036] Step S630: Reconstruct the full lifecycle data topology graph through the continuity verification of the chain incremental hash, and detect any hash link break event.

[0037] Furthermore, when a hash link break is detected, the cross-chain arbitration contract is activated to repair data consistency based on the verification results of the majority of sidechain nodes, and the repair record is written to the immutable log.

[0038] A digital artwork blockchain authentication and traceability system includes: a dynamic data capture engine module for real-time capture of multimodal dynamic data streams, the module including:

[0039] Entropy monitoring unit: calculates the information entropy of the user uploaded image data stream

[0040] Adaptive framing controller: performs entropy-driven framing threshold adjustment

[0041] Generative Adversarial Network Processing Unit: Implementation Composition operation

[0042] The chain evidence processing module is connected to the dynamic data capture engine module and includes:

[0043] Homomorphic Compression Unit: Deployment of Lattice-Based Cryptography algorithm

[0044] Timestamp-Version Indexer: Generate Double label

[0045] The cross-chain routing collaboration module is connected to the chain evidence processing module, including:

[0046] Smart Contract Gateway: Deploying a Cross-Chain Timelock Protocol

[0047] Multi-chain oracle interface: supports real-time synchronization of user-uploaded image verification results

[0048] The dynamic traceability execution module is connected to the cross-chain routing collaboration module and includes:

[0049] Multi-chain query agent: analyzing the full life cycle topology diagram

[0050] Topology rendering engine: Visualizing hash link nodes of digital artwork

[0051] Furthermore, the generative adversarial network processing unit configures a spatial transformation network to implement affine alignment operations:

[0052] ,

[0053] in, : Homogeneous coordinates of the original image pixels;

[0054] : Transformed digital artwork coordinates;

[0055] : The affine transformation matrix to be solved ( );

[0056] : Key points of clothing (such as neckline and shoulder line);

[0057] : Digital artwork template preset anchor point;

[0058] : spatial transformation network operation function;

[0059] The multi-chain oracle interface supports mapping compliance verification events of user-uploaded images to sidechain shard nodes.

[0060] The beneficial effects of the present invention are as follows: This solution dynamically tracks the state changes of digital artworks throughout their life cycle through adaptive framing compression of multimodal data streams and a chained incremental hash algorithm, ensuring tamper-proofing and high storage efficiency. It combines a lightweight cross-chain routing protocol with a layered state synchronization engine to achieve real-time collaborative verification of multi-chain data and low communication overhead. At the same time, through verifiable privacy labels and homomorphic desensitization technology, it meets compliance audits while protecting user sensitive information, and relies on the full life cycle data topology map to provide intuitive copyright traceability support, significantly improving the efficiency and security of digital content management in scenarios such as AI-generated art and metaverse virtual assets. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a workflow diagram of the method for blockchain authentication and traceability of digital artworks of the present invention;

[0062] Figure 2 This is a block diagram of the digital artwork blockchain authentication and traceability system of the present invention. DETAILED DESCRIPTION

[0063] The specific embodiments of the present invention are further described below with reference to the accompanying drawings, wherein the same parts are represented by the same reference numerals.

[0064] It should be noted that the words "front", "rear", "left", "right", "up" and "down" used in the following description refer to directions in the accompanying drawings, and the words "inside" and "outside" refer to directions toward or away from the geometric center of a specific component, respectively.

[0065] In order to make the contents of the present invention more clearly understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0066] Example 1

[0067] The present invention provides a method and system for blockchain authentication and traceability of digital artworks. The following example uses a user uploading a formal photo to generate a dynamic digital brooch.

[0068] Step 1: User upload and data preprocessing

[0069] Manual operation: The user selects a formal photo through the interactive interface, in JPEG / PNG format with a resolution of ≥1920×1080, to trigger the upload operation;

[0070] Image decoding: Parse the uploaded file into an RGB pixel matrix and extract metadata such as the camera device and GPS coordinates;

[0071] Privacy desensitization: Use selective homomorphic desensitization to generate verifiable privacy labels: in is a random salt value, Using BLAKE2b algorithm

[0072] Step 2: AI Compliance Review

[0073] Clothing feature extraction: load pre-trained convolutional neural network and calculate formal wear compliance score,

[0074] Trigger audit events: When threshold conditions are met When the system is in operation, it is allowed to enter the subsequent process;

[0075] Step 3: Digital Artwork Compositing

[0076] Manual operation: The user selects a virtual brooch template and completes payment;

[0077] Generative Adversarial Network Synthesis: Using a Spatial Transformer Network to Perform Affine Alignment: ,

[0078] in, : Homogeneous coordinates of the original image pixels;

[0079] : Transformed digital artwork coordinates;

[0080] : The affine transformation matrix to be solved ( );

[0081] : Key points of clothing (such as neckline and shoulder line);

[0082] : Digital artwork template preset anchor point;

[0083] : spatial transformation network operation function;

[0084] Output the composite image to ensure that the digital brooch fits the wrinkled area of ​​the clothing;

[0085] Step 4: Blockchain evidence processing

[0086] Adaptive framing compression:

[0087] Dynamically adjust the framing threshold according to the entropy value: ;

[0088] in is the information entropy, is a nonlinear function;

[0089] When the cumulative change Time-segmented state frame;

[0090] Chained incremental hashing:

[0091] Generate an irreversible hash link: ;

[0092] in It is a homomorphic compression algorithm.

[0093] Step 5: Cross-chain evidence storage and traceability

[0094] Multi-link routing distribution:

[0095] Decompose cross-chain routing labels and distribute them to heterogeneous sidechain node groups;

[0096] Dynamic traceability verification:

[0097] Analyze multi-chain hash chains and reconstruct the full life cycle data topology;

[0098] When a hash link break is detected, the cross-chain arbitration contract is activated to repair the data.

[0099] Through this technical solution, the embodiment achieves the synergistic effect of improving efficiency, enhancing security and optimizing user experience in all aspects of digital artwork generation, storage and traceability. It is particularly suitable for high-value scenarios such as metaverse virtual assets, e-commerce dynamic marketing, and digital copyright management.

[0100] Example 2

[0101] This embodiment focuses on the purchase of physical artworks and blockchain traceability

[0102] Step s1: Multimodal dynamic data capture and fusion

[0103] Physical artwork data collection

[0104] User-uploaded image stream: Buyers use their mobile devices to take high-definition photos of physical artworks (such as bronze sculptures), with a resolution of at least 4032 × 3024 pixels. A pre-trained ResNet-50 convolutional neural network is used to extract the multi-scale feature vectors of the artworks. The calculation formula is as follows:

[0105]

[0106] in, Taking as input an image matrix, the network focuses on the texture details (such as surface oxidation) and geometric forms (such as contour curvature) of the artwork.

[0107] Interaction behavior flow: records user operation events, including clicking the purchase button, filling in the delivery address, and selecting the payment method. Generates structured logs, and the event identifier encoding formula is:

[0108]

[0109] Sensor data stream: Reads data from the NFC chip built into the artwork, including:

[0110] Unique identifier: ART-2023-8765

[0111] Environmental records: temperature of the production environment and humidity

[0112] Logistics track: The accelerometer and GPS are used to generate a GeoHash-encoded location sequence. The quantization formula is:

[0113] Dynamic weight fusion dynamically allocates weights to each modality based on information entropy, giving priority to processing high-information data:

[0114]

[0115] Image flow weight:

[0116] Sensor stream weights:

[0117] Step S2: Adaptive frame compression and digital twin synthesis

[0118] Entropy-driven framing

[0119] Dynamic framing threshold adjustment: Dynamically set the framing trigger conditions based on the entropy changes of the sensor data stream (logistics trajectory): When the cumulative displacement change satisfies (For example ), split and generate logistics status frame .

[0120] Chained incremental hashing

[0121] Perform homomorphic encryption and hash binding on each state frame to ensure that the data cannot be tampered with:

[0122]

[0123] in, This is a UNIX timestamp (millisecond accuracy). A broken hash link will trigger a global failure.

[0124] Digital Twin Synthesis

[0125] Spatial Transformation Network Alignment: Align the physical photo with the 3D model and solve the affine transformation matrix:

[0126]

[0127] GAN material optimization: Improve the physical authenticity of digital copies through generative adversarial networks. The objective function is:

[0128] Step 3: Structured authentication object construction and privacy protection

[0129] Meta-feature spatiotemporal binding

[0130] Standardized event identifier: associates purchase events with logistics tracks. The encoding formula is:

[0131] Differential coding compression: Lightweight processing of sensor data streams to reduce storage overhead: ;

[0132] Selective privacy desensitization

[0133] Homomorphic encryption: Encrypt the buyer's ID number to protect sensitive information: ;

[0134] Verifiable privacy labels: Generate auditable, desensitized credentials to ensure on-chain data compliance: ;

[0135] Step S4: Cross-chain distribution and multi-chain verification

[0136] Technical implementation:

[0137] Main chain smart contract verification

[0138] Format compliance verification: The main chain smart contract checks the format of the evidence object. If the feature vector norm exceeds the limit or the signature is invalid, a cross-chain rollback is triggered:

[0139] Lightweight cross-chain routing

[0140] Shard routing decomposition: decompose cross-chain routing labels into target chain shard instructions, for example:

[0141] Data transmission optimization: Libp2p protocol is used to broadcast data packets, with a single packet size of ≤512KB and an end-to-end delay of ≤800ms.

[0142] Zero-knowledge proof verification: The logistics chain node generates Groth16 zero-knowledge proof to verify the integrity of multimodal data:

[0143]

[0144] Step S5: Multi-chain consensus and data repair

[0145] Technical implementation:

[0146] Cross-chain time lock protocol

[0147] Consensus window setting: The main chain sets a 30-minute time lock and monitors the side chain verification results. When more than 80% of the nodes confirm the validity, the verification hash will be written back to the main chain:

[0148] Arbitration Repair Mechanism

[0149] Majority node repair: If a logistic track hash break is detected (e.g. ), rebuild consistency based on majority node data:

[0150] Immutable logging: Repair operations are recorded and written to IPFS, with the content identifier:

[0151] Step S6: Full life cycle traceability and visualization

[0152] Technical implementation:

[0153] Dynamic topology reconstruction

[0154] Hash link continuity verification: extract all hash nodes from the main chain and side chain, and reconstruct the full life cycle topology in chronological order. The break detection formula is:

[0155] Visual rendering: The topology map is rendered using the WebGL engine. Broken nodes are displayed as red flashing icons, and complete links are displayed as green lines.

[0156] In this embodiment, the production, logistics, and transaction data of physical artworks are bound through chain hashing. Any physical replacement (such as replacing counterfeits during transportation) or data tampering will destroy the hash continuity and trigger a real-time alarm. The lightweight cross-chain routing protocol reduces the multi-chain verification delay to ≤800ms, supporting high-concurrency transaction scenarios (such as real-time bidding at auction houses). Through homomorphic desensitization technology, buyers' sensitive information (such as ID numbers and geographic locations) can be verified but not decrypted on the chain, complying with privacy regulations such as GDPR and CCPA. The digital copies generated by the spatial transformation network and GAN optimization have a material reflectivity error of ≤2% and a geometric alignment accuracy of ±0.1 pixel, meeting authentication-level requirements.

[0157] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention shall be included in the scope of protection of the present invention.

Claims

1. A method for blockchain authentication and traceability of digital artworks, characterized by: The following steps are involved: Step S100: Real-time capture of a multimodal dynamic data stream of a dynamic digital artwork, wherein the multimodal dynamic data stream includes a user-uploaded image data stream, a user interaction behavior data stream, an embedded sensor trigger data stream, and an AI algorithm self-iteration parameter stream, wherein the user-uploaded image data stream is used to extract a clothing feature vector through a convolutional neural network; Step S200: Adaptive frame compression is performed on the fused multimodal dynamic data stream to generate a continuous state frame sequence with timestamp-version dual indexes, and an irreversible state link is generated through a chained incremental hash algorithm. Digital artwork synthesis based on a generative adversarial network is performed on the user-uploaded image data stream; Step S300: Based on the Verifiable Data Object Protocol, the state frame sequence is bound to the meta-features of the multimodal dynamic data stream to construct a structured authentication object including the main chain evidence anchor, cross-chain routing tags, and traceable data fingerprints; Step S400: Write the main chain evidence anchor point into the main chain smart contract, and distribute the structured authentication object to the corresponding heterogeneous side chain node group through the lightweight cross-chain routing protocol, triggering multi-chain parallel consensus verification; Step S500: After the main chain and the side chain group are verified, the verification results are written back to the main chain evidence anchor point based on the hierarchical state synchronization engine, generating a dynamic traceability certificate including a cross-chain time lock and a multi-chain hash chain; Step S600: When responding to a traceability request, parse the dynamic traceability certificate and activate the multi-chain collaborative query agent to extract the full life cycle data topology of the embedded state frame sequence from the main chain and side chain group. The topology includes all hash link nodes in the digital artwork generation process.

2. The method for digital artwork blockchain authentication and traceability according to claim 1, characterized in that: The step S200 includes: Step S210: receiving a multimodal dynamic data stream, calculating its information entropy in real time, and dynamically adjusting the framing threshold based on the entropy value; Step S220: When the cumulative change of the data stream When , the framing operation is triggered to generate a status frame, and the framing logic is dynamically configured by the main chain smart contract through the oracle; Step S230: performing a chained incremental hash operation on each state frame to generate a hash link; Step S240: Output hash link and timestamp-version tag To the structured authentication object encapsulation process.

3. The method for digital artwork blockchain authentication and traceability according to claim 1, characterized in that: The step S300 includes: step S310: extracting interaction event feature vectors from the user interaction behavior data stream, and encoding the feature vectors into standardized event identifiers; Step S320: performing differential coding compression on the AI ​​algorithm self-iteration parameter stream to generate a lightweight parameter update package; Step S330: performing spatiotemporal association binding on the standardized event identifier, the lightweight parameter update package, and the hash link to generate a multimodal association metadata block; Step S340: Generate a main chain evidence anchor point based on the main chain smart contract address, and generate a cross-chain routing tag based on the side chain node topology structure to complete the encapsulation of the structured authentication object.

4. The method for digital artwork blockchain authentication and traceability according to claim 1, characterized in that: The step S400 includes: step S410: verifying the format compliance of the structured authentication object through the main chain smart contract, and triggering a cross-chain rollback instruction if the verification fails; Step S420: When the verification passes, the cross-chain routing label is decomposed into multiple sidechain shard routing instructions, and the structured authentication object is distributed to the target sidechain node group; Step S430: Using a zero-knowledge proof algorithm in the side chain node group to generate a compressed verification credential for the multimodal associated metadata block, and submitting the compressed verification credential to the side chain shard consensus network.

5. The method for digital artwork blockchain authentication and traceability according to claim 4, characterized in that: The step S500 includes: step S510: the main chain node monitors the verification results of the side chain shard consensus network, and when more than a preset proportion of the side chain shards pass the verification, the cross-chain time lock protocol is triggered; Step S520: During the validity period of the cross-chain time lock protocol, the verification hash of the side chain shard is written back to the main chain evidence anchor point to generate a multi-chain hash chain; Step S530: If the cross-chain time lock protocol times out and no consensus is reached, the cross-chain arbitration contract is activated to reallocate the verification task.

6. The method for digital artwork blockchain authentication and traceability according to claim 1, characterized in that: Step S300 also includes: Step S350: Perform selective homomorphic desensitization on sensitive fields in the user interaction behavior data stream to generate a verifiable privacy label ,in is a random salt value; Step S360: embed the verifiable privacy tag into the structured authentication object and declare the privacy verification policy in the dynamic traceability credential.

7. The method for digital artwork blockchain authentication and traceability according to claim 1, characterized in that: The step S600 includes: Step S610: Parse the multi-chain hash chain in the dynamic traceability certificate and obtain the initial version hash and ownership information from the main chain; Step S620: Locate the side chain shard node according to the cross-link routing label, and pull the incremental state frame sequence and associated metadata block; Step S630: Reconstruct the full lifecycle data topology graph through the continuity verification of the chain incremental hash, and detect any hash link break event.

8. The method for digital artwork blockchain authentication and traceability according to claim 1, characterized in that: When a hash link break is detected, the cross-chain arbitration contract is activated to repair data consistency based on the verification results of the majority of side chain nodes, and the repair record is written to the immutable log.

9. A digital artwork blockchain authentication and traceability system, used to implement the method described in any one of claims 1-8, characterized in that: include: The dynamic data capture engine module is used to capture multimodal dynamic data streams in real time. The module includes: Entropy monitoring unit: calculates the information entropy of the image data stream uploaded by the user; Adaptive framing controller: performs entropy-driven framing threshold adjustment of claim 2 Generative Adversarial Network Processing Unit: Implementation Composition operation The chain evidence processing module is connected to the dynamic data capture engine module and includes: Homomorphic Compression Unit: Deployment of Lattice-Based Cryptography algorithm Timestamp-Version Indexer: Generate Double label The cross-chain routing collaboration module is connected to the chain evidence processing module, including: Smart Contract Gateway: Deploying a Cross-Chain Timelock Protocol for Claim 5 Multi-chain oracle interface: supports real-time synchronization of user-uploaded image verification results The dynamic traceability execution module is connected to the cross-chain routing collaboration module and includes: Multi-chain query agent: parse the full life cycle topology diagram of claim 7; Topology rendering engine: Visualize the hash link nodes of digital artwork.

10. The digital artwork blockchain authentication and traceability system according to claim 9, characterized in that: The generative adversarial network processing unit configures a spatial transformation network to implement affine alignment operations, and the multi-chain oracle interface supports mapping compliance verification events of user-uploaded images to side chain sharding nodes.

Images