Block chain-based artwork anti-counterfeiting traceability system and method

Abstract

The invention relates to the technical field of block chain anti-counterfeiting, in particular to an artwork anti-counterfeiting traceability system and method based on a block chain, and the method comprises the steps: obtaining the multi-modal feature data of an artwork, and generating a unique digital fingerprint through a Hash algorithm; continuously acquiring a moving track image of the artwork and extracting spatial-temporal characteristics to generate a dynamic track password; encrypting the digital fingerprint, the track password and the artwork ownership information together, writing the encrypted information into a tamper-resistant distributed account book of a block chain, forming block chain data, and binding the block chain data with a two-dimensional code corresponding to the artwork; scanning the two-dimensional code attached to the artwork, calling the block chain data, and comparing the block chain data with the physical features and the track password to verify the artwork. Therefore, the position of the artwork can be continuously tracked in combination with the historical trajectory of the artwork, the artwork can be better traced through association between the position of the artwork and other anti-counterfeiting information of the artwork, and the transparency and credibility of an artwork transaction market are improved.

Description

Technical Field

[0001] The present invention relates to the field of blockchain anti-counterfeiting technology, and in particular to a blockchain-based artwork anti-counterfeiting and traceability system and method. Background Art

[0002] Artwork anti-counterfeiting and traceability refers to the use of modern information technology to establish a comprehensive identification and tracking system for artworks, ensuring their authenticity and traceability. By combining various technologies, such as blockchain, RFID tags, and digital watermarks, every movement of an artwork can be recorded in an immutable ledger. From the moment of creation to each transaction, all information can be effectively preserved and queried in real time.

[0003] Using the above method, the circulation information data of artworks can be established online, but it is not convenient to establish a strong correlation and correspondence with the actual artworks, which reduces the effectiveness of anti-counterfeiting and traceability. Summary of the Invention

[0004] The purpose of the present invention is to provide a blockchain-based art anti-counterfeiting and traceability system and method, which aims to combine the historical trajectory of the artwork to continuously track the location of the artwork, and better trace the artwork by associating the location of the artwork with other anti-counterfeiting information of the artwork, thereby improving the transparency and trust of the art trading market.

[0005] To achieve the above objectives, in a first aspect, the present invention provides a blockchain-based method for anti-counterfeiting and traceability of artworks, comprising obtaining multimodal feature data of the artwork and generating a unique digital fingerprint through a hash algorithm;

[0006] Continuously acquire the movement trajectory images of artworks and extract spatiotemporal features to generate dynamic trajectory codes;

[0007] The digital fingerprint and the track password are encrypted together with the artwork ownership information and written into the tamper-proof distributed ledger of the blockchain to form blockchain data and bind it to the QR code corresponding to the artwork;

[0008] Scan the QR code attached to the artwork to retrieve the blockchain data and compare it with the physical features and trajectory password to verify the artwork.

[0009] The multimodal feature data includes visible light images and infrared images.

[0010] The specific steps of obtaining the multimodal feature data of the artwork and generating a unique digital fingerprint through a hash algorithm include:

[0011] Collect visible light images of artworks and use infrared spectrometer to scan the surface of artworks to obtain infrared images;

[0012] Normalize the visible light image and infrared image to a uniform size and convert them into gray value matrices;

[0013] The total feature matrix is ​​obtained by extracting the deep features in the gray value matrix through the VGG16 network;

[0014] The digital fingerprint is obtained by processing the total feature matrix through the hash function.

[0015] The specific steps of continuously acquiring the movement trajectory image of the artwork and extracting the spatiotemporal features to generate the dynamic trajectory password include:

[0016] Acquire the artwork's movement path data through the accelerometer and gyroscope;

[0017] Extract the timestamps and geographic coordinates of key nodes in the mobile path data to obtain node information;

[0018] A dynamic trajectory password is generated based on the node information using a chaotic encryption algorithm.

[0019] After generating a dynamic trajectory password using a chaotic encryption algorithm based on the node information, the step further includes:

[0020] An auxiliary verification code is generated based on the environmental data collected by the environmental sensor of the artwork. The environmental data includes temperature data and humidity data. During verification, the dynamic trajectory password and the auxiliary verification code must be matched simultaneously.

[0021] The specific steps of obtaining the movement path data of the artwork using the acceleration sensor and the gyroscope include:

[0022] Record geographic data at preset time intervals through the positioning module;

[0023] If the positioning module signal is lost, it will automatically switch to indoor positioning and obtain the corresponding geographic data;

[0024] Split the geographic data by time window to obtain movement path data.

[0025] The specific steps of extracting the timestamps and geographic coordinates of key nodes in the movement path data to obtain node information include:

[0026] Cleaning of mobile path data, including filtering invalid data, deleting GPS drift points, and using cubic spline interpolation to fill in missing time periods;

[0027] Identify the location of stay points through spatiotemporal clustering algorithm;

[0028] Match the stop point locations with the geographic fences and exhibition areas to obtain key node locations.

[0029] The specific steps of generating the auxiliary verification code based on the environmental data of the artwork collected by the environmental sensor include:

[0030] Collecting data about the environment in which the artwork is located based on environmental sensors;

[0031] extracting environmental characteristics based on the environmental data, the environmental characteristics including average temperature and humidity change rate;

[0032] Combine the selected environmental features and convert them into a fixed-length string through a hash function;

[0033] Use a symmetric encryption algorithm to encrypt the string and obtain an auxiliary verification code.

[0034] In a second aspect, the present invention also provides a blockchain-based artwork anti-counterfeiting and traceability system, comprising an artwork fingerprint generation module, a trajectory password generation module, a blockchain generation module, and a verification module;

[0035] The artwork fingerprint generation module is used to obtain multimodal feature data of the artwork and generate a unique digital fingerprint through a hash algorithm;

[0036] The trajectory password generation module is used to continuously obtain the movement trajectory image of the artwork and extract the spatiotemporal features to generate a dynamic trajectory password;

[0037] The blockchain generation module is used to encrypt the digital fingerprint and the trajectory password together with the artwork ownership information and write them into the tamper-proof distributed ledger of the blockchain to form blockchain data and bind it to the QR code corresponding to the artwork;

[0038] The verification module is used to scan the QR code attached to the artwork to retrieve the blockchain data and compare it with the physical features and trajectory password to verify the artwork.

[0039] The blockchain-based artwork anti-counterfeiting and traceability system and method of the present invention obtains various characteristic data of an artwork, including but not limited to multimodal information such as image, material, and craftsmanship, and converts this information into a unique digital fingerprint using a hash algorithm. This digital fingerprint serves as a unique identifier for the artwork, ensuring its irreproducibility and uniqueness. To enhance the security of the artwork during its circulation, the method also includes continuous tracking of the artwork's movement trajectory. By capturing images of the artwork at different times and locations, the temporal and spatial characteristics contained therein are extracted to generate a dynamic trajectory code. This code not only records the artwork's historical location but also reflects its evolution in time and space, providing a more robust basis for verifying the artwork's authenticity. The generated digital fingerprint, along with the dynamic trajectory code and the artwork's ownership information, is encrypted into the blockchain system. Blockchain, as a decentralized and tamper-proof distributed ledger technology, ensures the security and transparency of this information. Once information is recorded on the blockchain, any attempt to modify this data becomes impossible, significantly enhancing the authenticity and reliability of the artwork information. To facilitate user query and verification, a unique QR code is generated for each artwork. Users simply scan the QR code attached to the artwork to quickly retrieve relevant data stored on the blockchain and compare it with the physical features and dynamic trajectory password to confirm the artwork's authenticity and ownership. This allows for continuous location tracking of the artwork, combined with its historical trajectory. By correlating the artwork's location with other anti-counterfeiting information, the artwork's provenance can be better traced, enhancing transparency and trust in the art market. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 This is a flowchart of a blockchain-based artwork anti-counterfeiting and traceability method of the present invention.

[0042] Figure 2 This is a flowchart of the present invention for obtaining multimodal feature data of an artwork and generating a unique digital fingerprint through a hash algorithm.

[0043] Figure 3 This is a flow chart of the present invention for continuously acquiring movement trajectory images of artworks and extracting spatiotemporal features to generate dynamic trajectory passwords.

[0044] Figure 4 This is a flow chart of the present invention for obtaining movement path data of an artwork through an acceleration sensor and a gyroscope.

[0045] Figure 5 This is a flow chart of the present invention for extracting the timestamps and geographic coordinates of key nodes in mobile path data to obtain node information.

[0046] Figure 6 This is a flow chart of the present invention for generating an auxiliary verification code based on environmental data collected by an environmental sensor about the environment in which the artwork is located. DETAILED DESCRIPTION

[0047] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0048] First embodiment

[0049] See also Figures 1 to 6 The present invention provides a blockchain-based method for tracing the source of artworks, comprising:

[0050] S101 obtains multimodal feature data of the artwork and generates a unique digital fingerprint through a hash algorithm;

[0051] The multimodal feature data includes visible light images and infrared images.

[0052] The specific steps include:

[0053] S201 collects visible light images of artworks and uses an infrared spectrometer to scan the surface of the artwork to obtain infrared images;

[0054] Use a high-resolution camera (such as a DSLR or industrial-grade camera) to photograph the artwork, ensuring even lighting distribution and avoiding shadows or overexposure that could affect image quality. Shooting should be perpendicular to the artwork's surface to minimize perspective distortion. Use an infrared spectrometer (such as a near-infrared or short-wave infrared imaging device) to scan the artwork's surface. Infrared images can capture information hidden beneath the surface, such as paint layer thickness, cracks, and signs of restoration, providing greater detail than visible light.

[0055] S202 normalizes the visible light image and the infrared image to a uniform size and converts them into a gray value matrix;

[0056] The captured visible light and infrared images are cropped to the same size and normalized. The purpose of normalization is to eliminate brightness differences caused by different devices and limit the image range to [0, 255] or [0, 1].

[0057] Convert the color image (if the visible light image is in color) and the infrared image to a grayscale image. The grayscale conversion formula is usually:

[0058] Gray=0.299R+0.587G+0.114BGray=0.299R+0.587G+0.114B

[0059] Where R, G, BR, G, B represent the pixel values ​​of the red, green, and blue channels respectively. Convert the grayscale image into a two-dimensional matrix, where each element represents the grayscale value of the pixel at the corresponding position.

[0060] For example, for a visible light image and an infrared image with a resolution of 1024×768, after normalization, they are adjusted to a uniform size of 256×256, and two 256×256 grayscale value matrices are generated respectively.

[0061] S203 extracts deep features from the grayscale value matrix through the VGG16 network to obtain a total feature matrix;

[0062] Use a deep learning framework such as TensorFlow or PyTorch to load a pre-trained VGG16 model. VGG16 is a classic convolutional neural network with powerful feature extraction capabilities. Feed the grayscale value matrix as input to the VGG16 network. Since VGG16 was originally designed for RGB images, the grayscale image needs to be expanded to a three-channel input (that is, the grayscale matrix needs to be replicated three times). Extract the feature vector at a certain layer of VGG16 (usually the layer before the fully connected layer). Assuming that a feature vector of length 4096 is extracted for each image, the feature vectors of the two images (visible light and infrared) are concatenated to form a total feature matrix of length 8192.

[0063] The visible light image and infrared image are fed into VGG16 respectively to obtain two feature vectors:

[0064] Visible light image feature vector: [0.1, 0.5, ..., 0.9] [0.1, 0.5, ..., 0.9] (length 4096)

[0065] Infrared image feature vector: [0.3, 0.2, ..., 0.7] [0.3, 0.2, ..., 0.7] (length 4096)

[0066] After concatenating the two, we get the total feature matrix:

[0067] [0.1, 0.5, ..., 0.9, 0.3, 0.2, ..., 0.7]

[0068] S204 processes the total feature matrix through a hash function to obtain a digital fingerprint.

[0069] Use a secure hash algorithm (such as SHA-256 or MD5) to hash the total feature matrix. The characteristic of a hash algorithm is that a slight change in the input will produce a completely different output, and it is irreversible.

[0070] The total feature matrix is ​​serialized into a string format and then input into a hash function to obtain a fixed-length digital fingerprint. For example, SHA-256 will generate a 256-bit binary string or its corresponding hexadecimal representation.

[0071] Assume that the total feature matrix is:

[0072] [0.1, 0.5, 0.9, 0.3, 0.2, 0.7]

[0073] It is serialized into the string "0.1, 0.5, 0.9, 0.3, 0.2, 0.7" and then a digital fingerprint is generated using the SHA-256 hash algorithm.

[0074] S102 continuously acquires movement trajectory images of artworks and extracts spatiotemporal features to generate dynamic trajectory codes;

[0075] The specific steps include:

[0076] S301 obtains the movement path data of the artwork through the acceleration sensor and gyroscope;

[0077] Embed an intelligent sensor module inside the artwork packaging, including a three-axis accelerometer and a three-axis gyroscope. These sensors can monitor the artwork's motion in real time, such as acceleration changes, azimuth angle, and rotation angle. The specific steps include:

[0078] S401 records geographic data every preset time period through the positioning module;

[0079] Use the GPS module to periodically record the artwork's geographic location information (longitude, latitude, altitude). The preset time interval is usually 1 to 10 seconds, depending on the application scenario.

[0080] Assume that the artwork records its location data every 5 seconds during transportation:

[0081] Time point t1: longitude = 116.397, latitude = 39.916;

[0082] Time point t2: longitude = 116.398, latitude = 39.917.

[0083] If the positioning module signal is lost, the system automatically switches to indoor positioning and obtains the corresponding geographic data.

[0084] When the GPS module cannot receive satellite signals (such as when entering a building or tunnel), the system will automatically switch to the backup positioning solution.

[0085] Wi-Fi positioning uses the MAC addresses and signal strength of surrounding Wi-Fi hotspots to estimate location. Bluetooth beacon positioning uses triangulation of deployed Bluetooth beacons to determine location. Inertial navigation combines data from accelerometers and gyroscopes to calculate relative displacement.

[0086] Seamlessly connect indoor positioning data with previous GPS data to form complete movement path data.

[0087] When an artwork enters a museum, the GPS signal is lost and the system switches to Wi-Fi positioning, recording the following data:

[0088] Time point t3: Location = Entrance of Exhibition Hall A of the Museum.

[0089] S403 divides the geographic data into time windows to obtain movement path data.

[0090] The geographic data is divided into multiple segments based on a preset time window (such as 30 seconds or 1 minute). Each segment contains all geographic coordinates within that time period.

[0091] Interpolate or fit the geographic data within each time window to generate a smooth movement path. Output the path data within each time window for subsequent spatiotemporal feature extraction.

[0092] Assume that the artwork records the following position data within 1 minute:

[0093] Time point t1: longitude = 116.397, latitude = 39.916

[0094] Time point t2: longitude = 116.398, latitude = 39.917

[0095] Time point t3: longitude = 116.399, latitude = 39.918

[0096] After time window segmentation and path fitting, a smooth path from the starting point to the end point is obtained.

[0097] S302 extracts the timestamps and geographic coordinates of key nodes in the movement path data to obtain node information;

[0098] The specific steps include:

[0099] S501 cleans the mobile path data, including filtering invalid data, deleting GPS drift points, and using cubic spline interpolation to fill in missing time periods;

[0100] Check the validity of each record, such as whether there are outliers (such as longitude outside the range [-180, 180][-180, 180] or latitude outside the range [-90, 90][-90, 90]). Delete data with duplicate or missing timestamps. Use a speed threshold method to detect drift points. Assuming that the average movement speed of an artwork cannot exceed a certain upper limit (such as 100km / h), if the speed between two adjacent locations far exceeds this upper limit, it is considered a drift point. After deleting drift points, retain the valid geographic coordinate data.

[0101] For missing data caused by signal loss, cubic spline interpolation is used to generate a smooth trajectory curve. Cubic spline interpolation can reasonably estimate the position of the missing period while ensuring continuity and smoothness.

[0102] S502 identifies the location of the stay point by using a spatiotemporal clustering algorithm;

[0103] Use density-based clustering algorithms (such as DBSCAN) to cluster the mobile path data. DBSCAN can divide dense points into clusters according to time and space dimensions, thereby identifying stay points.

[0104] In the parameter settings, the spatial distance threshold is set to 50 meters to identify the same location. The time window is set to 5 minutes or more to consider a stay point as valid. For each cluster, the geographic coordinates and time range of the center point are calculated as the location and duration of the stay point.

[0105] Assume that a certain moving path data contains the following points:

[0106] P1: longitude = 116.397, latitude = 39.916, time = t1;

[0107] P2: longitude = 116.3972, latitude = 39.9161, time = t2;

[0108] P3: longitude = 116.3973, latitude = 39.9162, time = t3;

[0109] After clustering through DBSCAN, a stop point is identified:

[0110] Stop location longitude = 116.3971, latitude = 39.9161

[0111] The dwell time range is [t1, t3].

[0112] S503 matches the location of the stop point with the geographic fence and the exhibition area to obtain the key node location.

[0113] Several geo-fenced areas are pre-defined, such as museum entrance, warehouse boundary, exhibition hall, etc. Each geo-fence is represented by a polygon consisting of a set of geographic coordinates.

[0114] The geographic coordinates of each dwell point are matched to the geofences. If the dwell point is within a geofence, it is marked as a key node corresponding to the fence.

[0115] If the stop point was not within any predefined geofence, it was manually annotated based on its surroundings.

[0116] Output the timestamp, geographic coordinates, and region of each key node.

[0117] Assume that a museum's geofence is defined as follows:

[0118] Exhibition Hall A: [(116.397, 39.916), (116.398, 39.916), (116.398, 39.917), (116.397, 39.917)]

[0119] Warehouse B: [(116.400, 39.915), (116.401, 39.915), (116.401, 39.916), (116.400, 39.916)]

[0120] A stop location is:

[0121] Longitude = 116.3971, Latitude = 39.9161

[0122] After matching, it is confirmed that the stop point belongs to Exhibition Hall A, and the final output is:

[0123] Key node information: time = t2, location = (116.3971, 39.9161), area = Exhibition Hall A.

[0124] S303 generates a dynamic trajectory password using a chaotic encryption algorithm based on the node information.

[0125] The timestamps and geographic coordinates of the key nodes obtained from S302 are used as input data. This information represents some important locations during the movement of the artwork. The chaotic encryption algorithm relies on the characteristics of chaotic systems, namely, they are extremely sensitive to initial conditions and can produce seemingly random but actually completely repeatable results. Commonly used chaotic systems include logistic maps and Chua circuits. Certain features in the node information (such as timestamps or specific parts of geographic coordinates) are used as the initial values ​​of the chaotic system. This step ensures that each generated dynamic trajectory password is unique.

[0126] According to the iterative calculation of the selected chaotic system, the node information is processed by the system in sequence to obtain a series of chaotic sequences. These chaotic sequences are then converted into binary strings to form a dynamic trajectory password.

[0127] S304 generates an auxiliary verification code based on the environmental data of the artwork collected by the environmental sensor. The environmental data includes temperature data and humidity data. During verification, the dynamic trajectory password and the auxiliary verification code must be matched simultaneously.

[0128] The specific steps include:

[0129] S601 collects environmental data of the artwork based on environmental sensors;

[0130] Install high-precision temperature and humidity sensors in environments where artworks are stored or transported. These sensors should be able to monitor and record the temperature and humidity of the location in real time. Set the data collection frequency based on actual needs, for example, every 5 minutes, to ensure that changing trends in environmental conditions are captured. Collected data should be transmitted to a central server via a wireless network or other means for processing. To ensure data authenticity, encrypted transmission technology can be used to prevent data tampering.

[0131] Assume that an artwork is being transported and the environmental sensor records temperature and humidity data every 5 minutes:

[0132] Time point t1: Temperature = 22°C, Humidity = 45%

[0133] Time point t2: temperature = 21.8°C, humidity = 46%.

[0134] S602 extracts environmental features based on the environmental data, the environmental features including average temperature and humidity change rate;

[0135] All temperature readings within a selected time period are averaged to obtain the average temperature for that period. The change in humidity between adjacent time points is calculated and then divided by the time interval to obtain the humidity change rate. This can help identify periods of large humidity fluctuations, which may indicate an unstable environment.

[0136] For the two sets of data above, suppose we focus on the time period from t1 to t2:

[0137] Average temperature = (22 + 21.8) / 2 = 21.9 (22 + 21.8) / 2 = 21.9°C

[0138] Humidity change rate = (46-45) / 5 = 0.2% / minute

[0139] S603 combines the selected environmental features and converts them into a fixed-length string through a hash function;

[0140] The calculated average temperature and humidity change rate are combined into a feature vector. For example, if the average temperature is 21.9°C and the humidity change rate is 0.2% / minute, the feature vector can be represented as [21.9, 0.2].

[0141] The feature vector is hashed using a secure hash algorithm (such as SHA-256) to convert it into a fixed-length string. This ensures that even if the input changes slightly, the output will be significantly different, thereby increasing the security of the CAPTCHA.

[0142] S604 uses a symmetric encryption algorithm to encrypt the character string and obtain an auxiliary verification code.

[0143] Select a symmetric encryption algorithm (such as AES) and determine the key. Symmetric encryption means that the same key is used for encryption and decryption, so secure key management is required. Use the selected key to encrypt the hash string obtained in the previous step. The encrypted result is the auxiliary verification code. During the verification phase, both the dynamic track password and the auxiliary verification code must be provided. The system first regenerates the auxiliary verification code based on the current environment data and compares it with the verification code provided. Verification is considered successful only if both match.

[0144] S103 encrypts the digital fingerprint and the track password together with the artwork ownership information and writes them into the tamper-proof distributed ledger of the blockchain to form blockchain data and bind it to the QR code corresponding to the artwork;

[0145] A unique identifier generated from the artwork's multimodal feature data (such as visible light and infrared images). A dynamic password generated based on key node information along the artwork's path. This includes the artwork's owner's identity information (such as name or institution name), transaction history, and current status.

[0146] Encrypt the prepared data using a symmetric encryption algorithm (such as AES) or an asymmetric encryption algorithm (such as RSA). To ensure security, it is recommended to use an asymmetric encryption algorithm, in which a public key is used for encryption and a private key is used for decryption.

[0147] If a symmetric encryption algorithm is used, a random key is generated and kept securely. If an asymmetric encryption algorithm is used, a public and private key pair is generated, where the public key is publicly distributed and the private key is kept by a trusted party (such as the artwork owner or regulatory agency). The data is encrypted using the selected encryption algorithm and key to obtain an encrypted string.

[0148] S104 scans the QR code attached to the artwork to retrieve the blockchain data and compares it with the physical features and trajectory password to verify the artwork.

[0149] Each artwork is accompanied by a unique QR code tag, which is encrypted and contains the artwork's unique identifier on the blockchain (such as a digital fingerprint or transaction hash value). QR codes usually use anti-counterfeiting technology (such as micro-printing, anti-copy coating, etc.) to prevent forgery or substitution.

[0150] Use a dedicated device that supports blockchain query functions (such as a smartphone or professional scanner) to scan the QR code attached to the artwork. During the scanning process, the device will interpret the information in the QR code and send it to the blockchain network for data query. Based on the identification information in the QR code, all stored data related to the artwork will be extracted from the blockchain ledger. This data includes but is not limited to:

[0151] Digital fingerprint: A unique identifier generated by the multimodal characteristics of an artwork.

[0152] Dynamic trajectory password: A dynamic password generated based on the movement path of the artwork.

[0153] Ownership information: records the ownership change history and current status of the artwork.

[0154] Environmental data: Environmental information such as temperature and humidity during the transportation or exhibition of artworks.

[0155] High-precision equipment is then used to recapture the artwork's multimodal feature data, such as visible light and infrared images. This data reflects the artwork's current physical condition. This collected multimodal feature data is processed using the same algorithmic process used for blockchain storage to generate a real-time digital fingerprint. This step ensures that the generated digital fingerprint is comparable to the original digital fingerprint stored on the blockchain.

[0156] The digital fingerprint generated in real time is compared with the digital fingerprint stored on the blockchain. If the two are identical, the physical condition of the artwork has not been altered. Discrepancies may indicate restoration, damage, or forgery. If a discrepancy is detected, the system triggers an alert and prompts the user to further verify the condition of the artwork. The system also records the verification results for subsequent tracking and analysis.

[0157] Using a GPS module or other positioning device, the artwork's current geographic location is captured and combined with its historical movement path to generate a new dynamic trajectory code. This new dynamic trajectory code is then compared with the historical trajectory code stored on the blockchain. If the two match, the artwork's movement path is as expected; any discrepancies may indicate unauthorized movement or tampering. If the trajectory codes do not match, the system further analyzes the cause of the discrepancy, for example, whether data is missing due to signal loss or whether there was illegal transportation. Based on the analysis results, the system generates a detailed report for reference.

[0158] After completing the comparison of the digital fingerprint and the dynamic trajectory password, the system will conduct a comprehensive evaluation of all verification results. Only when both verifications are passed can the artwork be considered authentic and complete. After the verification is completed, the system will provide clear feedback to the user. For example:

[0159] Verification passed: The artwork is authentic and no anomalies were found.

[0160] Partial anomalies: Some features or trajectories have slight differences and require further verification.

[0161] Verification failed: The artwork may have been forged, tampered with, or illegally moved, and immediate action is required.

[0162] Second embodiment

[0163] The present invention provides an artwork anti-counterfeiting and traceability system based on blockchain, comprising an artwork fingerprint generation module, a trajectory password generation module, a blockchain generation module and a verification module; the artwork fingerprint generation module is used to obtain multimodal feature data of the artwork and generate a unique digital fingerprint through a hash algorithm; the trajectory password generation module is used to continuously obtain a moving trajectory image of the artwork and extract spatiotemporal features to generate a dynamic trajectory password; the blockchain generation module is used to encrypt the digital fingerprint and the trajectory password together with artwork ownership information and write them into an unalterable distributed ledger of the blockchain to form blockchain data and bind it to a QR code corresponding to the artwork; the verification module is used to scan the QR code attached to the artwork to retrieve the blockchain data and compare it with the physical features and the trajectory password to verify the artwork.

[0164] In this embodiment, the artwork fingerprint generation module uses a high-resolution camera to capture visible light images of the artwork and an infrared spectrometer to scan the artwork surface to obtain infrared images. This multimodal feature data can reveal details of the artwork under different lighting conditions. The collected image data is resized and converted to grayscale values ​​to form a standardized input format for subsequent processing. A deep learning model (such as VGG16) is used to extract deep features from the grayscale image and convert these features into a unique digital fingerprint using a hashing algorithm. This digital fingerprint is the unique identity of the artwork.

[0165] The trajectory password generation module uses accelerometers and gyroscopes to record the artwork's position changes in real time during transportation or display. Furthermore, GPS positioning technology is used to regularly update geolocation information to ensure the accuracy and integrity of trajectory data. The collected geographic coordinates and timestamps are analyzed to identify key nodes (such as stopover points). Dynamic trajectory passwords are generated based on the temporal and spatial distribution characteristics of these nodes. This password not only reflects the artwork's actual movement path but also adds an additional layer of security.

[0166] The blockchain generation module combines the digital fingerprint and dynamic track password generated by the two modules above with the artwork's ownership information (such as the owner's name and transaction history), protects it using advanced encryption technology, and then writes it to the blockchain's distributed ledger. This ensures that the data cannot be tampered with. Each artwork is assigned a unique QR code that links to the relevant data on the blockchain. This is designed to allow anyone to easily access detailed information about the artwork by scanning the QR code.

[0167] When verifying an artwork, users can scan the attached QR code with a dedicated device to quickly access relevant information stored on the blockchain. The system recollects the artwork's multimodal feature data in its current state and generates a new digital fingerprint. Simultaneously, a new tracking password is generated based on the latest geolocation information. The new and old data are then compared to confirm the artwork's authenticity and any unauthorized movement. Based on the comparison results, the system issues a verification verdict. If everything is normal, the artwork is deemed authentic; any anomalies warrant further investigation.

[0168] This blockchain-based art anti-counterfeiting and traceability system integrates multiple processes, including artwork fingerprint generation, track password generation, blockchain writing, and verification, to effectively manage the entire life cycle of artworks. It not only improves the transparency of the art market but also significantly enhances the security and protection of artworks.

[0169] The above disclosure is only a preferred embodiment of the present invention, and certainly cannot be used to limit the scope of the rights of the present invention. Ordinary technicians in this field can understand that all or part of the processes of the above embodiment and equivalent changes made in accordance with the claims of the present invention are still within the scope of the invention.

Claims

1. A blockchain-based method for tracing the source of artworks, characterized by: include: Obtain multimodal feature data of artworks and generate unique digital fingerprints through hash algorithms; Continuously acquire the movement trajectory images of artworks and extract spatiotemporal features to generate dynamic trajectory codes; The digital fingerprint and the track password are encrypted together with the artwork ownership information and written into the tamper-proof distributed ledger of the blockchain to form blockchain data and bind it to the QR code corresponding to the artwork; Scan the QR code attached to the artwork to retrieve the blockchain data and compare it with the physical features and trajectory password to verify the artwork.

2. The blockchain-based artwork anti-counterfeiting and traceability method according to claim 1, characterized in that: The multimodal feature data includes visible light images and infrared images.

3. The blockchain-based artwork anti-counterfeiting and traceability method according to claim 2, characterized in that: The specific steps of obtaining the multimodal feature data of the artwork and generating a unique digital fingerprint through a hash algorithm include: Collect visible light images of artworks and use infrared spectrometer to scan the surface of artworks to obtain infrared images; Normalize the visible light image and infrared image to a uniform size and convert them into gray value matrices; The total feature matrix is ​​obtained by extracting the deep features in the gray value matrix through the VGG16 network; The digital fingerprint is obtained by processing the total feature matrix through the hash function.

4. The blockchain-based artwork anti-counterfeiting and traceability method according to claim 3, characterized in that: The specific steps of continuously acquiring the movement trajectory image of the artwork and extracting the spatiotemporal features to generate the dynamic trajectory password include: Acquire the artwork's movement path data through the accelerometer and gyroscope; Extract the timestamps and geographic coordinates of key nodes in the mobile path data to obtain node information; A dynamic trajectory password is generated based on the node information using a chaotic encryption algorithm.

5. The blockchain-based artwork anti-counterfeiting and traceability method according to claim 4, characterized in that: After generating a dynamic trajectory password based on the node information using a chaotic encryption algorithm, the step further includes: An auxiliary verification code is generated based on the environmental data collected by the environmental sensor of the artwork. The environmental data includes temperature data and humidity data. During verification, the dynamic trajectory password and the auxiliary verification code must be matched simultaneously.

6. The blockchain-based artwork anti-counterfeiting and traceability method according to claim 5, characterized in that: The specific steps of obtaining the movement path data of the artwork by using the acceleration sensor and the gyroscope include: Record geographic data at preset time intervals through the positioning module; If the positioning module signal is lost, it will automatically switch to indoor positioning and obtain the corresponding geographic data; Split the geographic data by time window to obtain movement path data.

7. The blockchain-based artwork anti-counterfeiting and traceability method according to claim 6, characterized in that: The specific steps of extracting the timestamps and geographic coordinates of key nodes in the movement path data to obtain node information include: Cleaning of mobile path data, including filtering invalid data, deleting GPS drift points, and using cubic spline interpolation to fill in missing time periods; Identify the location of stay points through spatiotemporal clustering algorithm; Match the stop point locations with the geographic fences and exhibition areas to obtain key node locations.

8. The blockchain-based artwork anti-counterfeiting and traceability method according to claim 7, characterized in that: The specific steps of generating the auxiliary verification code based on the environmental data of the artwork collected by the environmental sensor include: Collecting data about the environment in which the artwork is located based on environmental sensors; extracting environmental characteristics based on the environmental data, the environmental characteristics including average temperature and humidity change rate; Combine the selected environmental features and convert them into a fixed-length string through a hash function; Use a symmetric encryption algorithm to encrypt the string and obtain an auxiliary verification code.

9. A blockchain-based artwork anti-counterfeiting and traceability system, using a blockchain-based artwork anti-counterfeiting and traceability method according to any one of claims 1 to 8, characterized in that: It includes artwork fingerprint generation module, trajectory password generation module, blockchain generation module and verification module; The artwork fingerprint generation module is used to obtain multimodal feature data of the artwork and generate a unique digital fingerprint through a hash algorithm; The trajectory password generation module is used to continuously obtain the movement trajectory image of the artwork and extract the spatiotemporal features to generate a dynamic trajectory password; The blockchain generation module is used to encrypt the digital fingerprint and the trajectory password together with the artwork ownership information and write them into the tamper-proof distributed ledger of the blockchain to form blockchain data and bind it to the QR code corresponding to the artwork; The verification module is used to scan the QR code attached to the artwork to retrieve the blockchain data and compare it with the physical features and trajectory password to verify the artwork.

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