Data verification methods, apparatus, equipment, media and program products

Abstract

This application provides a data verification method, apparatus, device, medium, and program product, relating to the field of computer technology, for improving the data verification security of AIGC. The specific technical solution is as follows: obtaining a first AIGC generated by at least one generating device; obtaining metadata information corresponding to the first AIGC; wherein, the metadata information includes: the identity information of the generating device and the flow path log corresponding to the first AIGC; the identity information of the generating device includes at least one of the following: hardware root of trust information of each generating device, and a joint identity identifier of the at least one generating device; the hardware root of trust information is used to characterize the hardware information of the chip of the generating device, and the computing power characteristic information of the generating device when generating the first AIGC; based on the metadata information, performing data verification on the first AIGC, the data verification including: verifying whether the first AIGC has been tampered with, and verifying whether the generating device corresponding to the first AIGC is a trusted device. This application is applied to scenarios involving data verification of AIGC.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a data verification method, apparatus, device, medium, and program product. Background Technology

[0002] With the rapid development of Artificial Intelligence (AI) technology, AI-generated content (AIGC) has become an important data resource, widely used in many fields. Since the authenticity and integrity of AIGC directly determine the reliability and security of downstream AI systems, data tracing of AIGC to verify its authenticity and credibility has become extremely important.

[0003] In related technologies, the generating device can store key metadata in the generated AIGC in a blockchain network, and generate traceability credentials corresponding to the AIGC based on the key metadata and multiple timestamp information. This allows third-party devices to trace the flow path of the AIGC through the blockchain network based on the traceability credentials of the AIGC, in order to verify whether the AIGC has been tampered with.

[0004] Therefore, in implementing the above scheme, the verification of AIGC authenticity relies solely on tracing its flow path to confirm whether it has been tampered with. Consequently, the security of AIGC data verification in this technology is relatively low. Summary of the Invention

[0005] This application provides a data verification method, apparatus, device, medium, and program product for improving the security of AIGC data verification.

[0006] In a first aspect, embodiments of this application provide a data verification method, the method comprising: obtaining a first AIGC generated by at least one generating device; obtaining metadata information corresponding to the first AIGC; wherein the metadata information includes the identity information of the generating device and the flow path log corresponding to the first AIGC; the identity information of the generating device includes at least one of the following: hardware root of trust information of each generating device, and a joint identity identifier of the at least one generating device; the hardware root of trust information is used to characterize the hardware information of the chip of the generating device, and the computing power characteristic information of the generating device when generating the first AIGC; and performing data verification on the first AIGC based on the metadata information, the data verification including: verifying whether the first AIGC has been tampered with and verifying whether the generating device corresponding to the first AIGC is a trusted device.

[0007] The technical solution provided in this application brings at least the following beneficial effects: By writing the hardware information of the generating device corresponding to the AIGC and the flow path information of the AIGC into the metadata of the AIGC, the AIGC is bound together with the hardware information of its generating device. This allows the device to directly obtain the hardware information of the generating device that generated the AIGC and the flow path information of the AIGC based on the metadata information of the AIGC after obtaining the AIGC. Based on the hardware information and flow path information, it can verify whether the AIGC has been tampered with or whether the generating device is a counterfeit device. In this way, based on verifying whether the AIGC has been tampered with, it can verify whether the generating device corresponding to the AIGC is a counterfeit device through the hardware information of the generating device carried in the metadata information of the AIGC. This solves the problem of not being able to identify whether the generating device corresponding to the AIGC is a counterfeit device, thereby improving the security of data verification of AIGC.

[0008] One possible implementation of the above method further includes: generating a unique hardware fingerprint for each generating device using Physical Unclonable Functions (PUF) technology, wherein the unique hardware fingerprint represents the hardware information of the chip of the generating device; and generating hardware root of trust information for each generating device based on the unique hardware fingerprint of each generating device and the computing power feature information of each generating device when generating the first AIGC.

[0009] Another possible implementation is that the above-mentioned at least one generating device includes N generating devices, where N is an integer greater than 1; the above method further includes: authenticating the above-mentioned N generating devices through a certificateless cryptographic authentication method; after the above-mentioned N generating devices are successfully authenticated, generating a joint identity identifier for the above-mentioned N generating devices based on the hardware trusted root information of each generating device.

[0010] Another possible implementation is to perform data verification on the first AIGC based on the metadata information, including: verifying whether the generating device corresponding to the first AIGC is a trusted device based on the aforementioned generating device identity information, and verifying whether the first AIGC has been tampered with based on the flow path log corresponding to the first AIGC.

[0011] Another possible implementation is that the aforementioned computing power characteristic information includes at least one of the following: device computing latency information, device power consumption information, and device temperature information.

[0012] Secondly, embodiments of this application provide a data verification device, including: an acquisition module and a verification module; the acquisition module is used to acquire first artificial intelligence generated content (AIGC) generated by at least one generating device; and to acquire metadata information corresponding to the first AIGC; wherein the metadata information includes: identity information of the generating device and a flow path log corresponding to the first AIGC; the identity information of the generating device includes at least one of the following: hardware root of trust information of each generating device, and a joint identity identifier of the at least one generating device; the hardware root of trust information is used to characterize the hardware information of the chip of the generating device, and the computing power characteristic information of the generating device when generating the first AIGC; the verification module is used to perform data verification on the first AIGC based on the metadata information, the data verification including: verifying whether the first AIGC has been tampered with, and verifying whether the generating device corresponding to the first AIGC is a trusted device.

[0013] In one possible implementation, the above apparatus further includes: a generation module; the generation module is configured to generate a unique hardware fingerprint for each generation device using PUF technology, the unique hardware fingerprint representing the hardware information of the chip of the generation device; and to generate hardware root of trust information for each generation device based on the unique hardware fingerprint of each generation device and the computing power characteristic information of each generation device when generating the first AIGC.

[0014] In another possible implementation, the above apparatus further includes: a generation module; the at least one generation device includes N generation devices, where N is an integer greater than 1; the verification module is further configured to authenticate the N generation devices through a certificateless password authentication method; and the generation module is configured to generate a joint identity identifier for the N generation devices based on the hardware root of trust information of each generation device after the verification module has successfully authenticated the N generation devices.

[0015] Another possible implementation is that the verification module described above is specifically used to verify whether the generating device corresponding to the first AIGC is a trusted device based on the aforementioned generating device identity information, and to verify whether the first AIGC has been tampered with based on the flow path log corresponding to the first AIGC.

[0016] Another possible implementation is that the aforementioned computing power characteristic information includes at least one of the following: device computing latency information, device power consumption information, and device temperature information.

[0017] Thirdly, this application provides an electronic device comprising: a processor and a memory; the memory stores a program or instructions executable on the processor, wherein the program or instructions, when executed by the processor, implement the method of the first aspect described above.

[0018] Fourthly, this application provides a readable storage medium on which a program or instructions are stored, which, when executed by a computer, implement the method of the first aspect described above.

[0019] Fifthly, this application provides a computer program product stored in a storage medium, which, when executed by a computer, implements the method described in the first aspect.

[0020] In a sixth aspect, embodiments of this application provide a chip including a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the method described in the first aspect.

[0021] The beneficial effects of the second to sixth aspects mentioned above are described in the corresponding description of the first aspect and will not be repeated here. Attached Figure Description

[0022] Figure 1 A schematic diagram of the network architecture for a data verification method application provided in this application embodiment;

[0023] Figure 2 A flowchart illustrating a data verification method provided in an embodiment of this application;

[0024] Figure 3 A flowchart illustrating another data verification method provided in an embodiment of this application;

[0025] Figure 4 A flowchart illustrating another data verification method provided in this application embodiment;

[0026] Figure 5 A flowchart illustrating another data verification method provided in this application embodiment;

[0027] Figure 6 A flowchart illustrating the implementation process of a data verification method provided in this application embodiment;

[0028] Figure 7 This is a schematic diagram of the structure of a data verification device provided in an embodiment of this application;

[0029] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0030] The data verification methods, apparatus, equipment, media, and program products provided in this application will now be described in detail with reference to the accompanying drawings.

[0031] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0032] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0033] The terms "at least one," "at least one of," etc., used in the specification and claims of this application refer to any one, any two, or a combination of two or more of the included items. For example, at least one of a, b, and c can mean: "a," "b," "c," "a and b," "a and c," "b and c," and "a, b, and c," where a, b, and c can be single or multiple. Similarly, "at least two" refers to two or more items, and its meaning is similar to that of "at least one."

[0034] In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0035] The following explains some concepts and terms involved in the data verification methods, apparatus, devices, media, and program products provided in the embodiments of this application.

[0036] PUF (Physical Unknown Fingerprint) technology is an advanced security technology that generates unique fingerprints by utilizing the physical characteristics of hardware. It possesses six key properties: randomness, uniqueness, stability, immutability, physical non-cloning, and mathematical non-cloning. These properties make it ideal for generating unique physical fingerprints for devices. PUF technology leverages minute variations in semiconductor manufacturing processes, which naturally occur during chip production, to generate a unique identifier for each chip. This hardware-based fingerprint generation method is a crucial solution for achieving hardware security on resource-constrained electronic devices. A key advantage of PUF technology is that it eliminates the need for stored keys; instead, it dynamically generates keys by measuring the physical characteristics of the device. This generation process typically includes registration, anti-aging processing, physical fingerprint recovery, and the derivation of a unique hardware fingerprint. Because the physical characteristics of hardware are random and unpredictable, the generated unique hardware fingerprint also exhibits high randomness and unpredictability. Furthermore, PUF's tamper-resistance makes it ideal for device identification. In traceability and anti-counterfeiting scenarios, the unique identifier generated by PUF can be bound to the data generated by the device, enabling reliable tracking of the data's origin. This hardware-level security greatly enhances the system's credibility and provides a solid guarantee for the authenticity and integrity of the data.

[0037] Certificate-free cryptography is an innovative solution that lies between traditional Public Key Infrastructure (PKI) and identity cryptography. Compared to PKI, certificate-free cryptography eliminates the need for complex certificate management systems, significantly reducing system management and communication overhead. A user's key consists of two parts: one provided by a Key Generation Center (KGC), and the other generated by the user. This design ensures that even if one key is stolen, an attacker cannot reconstruct the other key from the stolen key, enhancing system security. Certificate-free cryptography avoids the inconvenience of traditional public key certificate schemes and the key escrow deficiencies of identity public key cryptography. However, like all public key schemes, the private key generated by the user and the system private key generated by the Key Generation Center are weak points in the system. Combining PUF technology with these systems offers a superior solution in terms of cost, process simplicity, and security.

[0038] Blockchain Distributed Evidence Storage Network: A blockchain distributed evidence storage network is a trusted data storage and verification system built on the decentralized consensus mechanism of blockchain. Multiple nodes jointly maintain a unified, immutable ledger, eliminating the need for a single centralized institution to provide trust endorsement. It relies on cryptographic algorithms to achieve the immutability and non-repudiation of data. All participating nodes synchronously store copies of the evidence data, and any attempt to tamper with the data by any node will be rejected due to failure to pass consensus verification. In scenarios such as AIGC traceability, this network can bind and store the entire chain of information, including the source identity, collaborative signatures, and flow logs, along with timestamps. It supports local verification by any node and tracing back along the identity chain to the original generating device, achieving trusted traceability throughout the entire data lifecycle.

[0039] The data verification method, apparatus, device, medium, and program product provided in this application embodiment can be applied to various scenarios with high security and high reliability requirements for the entire process of AIGC generation, transmission, and storage, especially scenarios involving cross-device collaborative computing and scenarios requiring hardware-level traceability of AIGC throughout its entire lifecycle.

[0040] Specifically, the application scenarios of this application embodiment include, but are not limited to, the following aspects:

[0041] 1. Smart healthcare scenarios

[0042] In the field of smart healthcare, AI-assisted diagnostic systems such as electrocardiographs, blood glucose monitors, and electronic medical record systems typically rely on AIGC generated by a generating device through a medical AI model to generate comprehensive diagnostic suggestions or health reports. The data verification method provided in this application embodiment can be applied to verify whether the AIGC generated by the generating device is authentic and reliable.

[0043] 2. Autonomous driving scenario

[0044] Autonomous driving systems are multi-device collaborative computing systems that require real-time fusion of data from LiDAR, millimeter-wave radar, high-definition cameras, inertial measurement units, and high-precision maps. An AIGC (Autonomous Controller Dataset) is then generated based on an autonomous driving artificial intelligence model by a generation device to inform driving decisions. The data verification method provided in this application can be used to verify the authenticity of the AIGC generated by the generation device.

[0045] In existing technologies, AIGC (AI Data Collection) is gradually becoming an important data resource in modern society, widely used in fields such as healthcare, autonomous driving, and intelligent manufacturing. The authenticity and integrity of AIGC data are directly related to the reliability and security of artificial intelligence systems. Deviations or errors in AIGC data can lead to serious consequences such as misdiagnosis in medical settings or incorrect decisions by autonomous driving systems. Therefore, ensuring the credibility of AIGC data sources, the integrity of its content, and its preservation from tampering or falsification during sharing and dissemination has become a core issue that urgently needs to be addressed.

[0046] To address the aforementioned issues, related technologies utilize blockchain technology to store AIGCs, extract key metadata, construct provenance data, apply for timestamp certificates from authoritative timestamp organizations, and embed them into the extended domain of the key metadata to generate complete provenance credentials. This solution provides AIGCs with full lifecycle provenance and authoritative third-party certification, along with multiple protection mechanisms. However, this solution only traces AIGCs at the data level, lacking hardware-level security. This results in the inability to reliably authenticate AIGCs at the hardware level, meaning it cannot ensure the authenticity and trustworthiness of AIGC origins. Furthermore, in scenarios where multiple devices collaboratively generate AIGCs, existing technologies lack effective traceability methods for data flow between devices, easily leading to problems such as unclear data sources and ambiguous responsibility. Additionally, existing technologies lack end-to-end trusted authentication and traceability records during AIGC transmission and flow, making data susceptible to tampering. Thus, the security of AIGC data verification in related technologies is relatively low.

[0047] To address the aforementioned technical problems, this application provides a data verification method, apparatus, device, medium, and program product. By writing the hardware information of the generating device corresponding to the AIGC and the AIGC's transfer path information into the AIGC's metadata, the AIGC is bound to the hardware information of its generating device. This allows the device, upon acquiring the AIGC, to directly obtain the hardware information of the generating device and the transfer path information of the AIGC based on its metadata. Based on this hardware information and transfer path information, the device can verify whether the AIGC has been tampered with or whether the generating device is a counterfeit device. Thus, based on verifying whether the AIGC has been tampered with, the device can also verify whether the generating device corresponding to the first AIGC is a counterfeit device by using the hardware information of the generating device carried in the AIGC's metadata. This solves the problem of not being able to identify whether the generating device corresponding to the AIGC is a counterfeit device, thereby improving the security of AIGC data verification.

[0048] The data verification methods, apparatus, devices, media, and program products provided in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0049] Figure 1 The network architecture for a data verification method provided in an embodiment of this application is illustrated. For example... Figure 1 As shown, the network architecture includes a data verification device 101 and a terminal device 102. The data verification device 101 and the terminal device 102 are interconnected.

[0050] In some embodiments, the data verification device 101 may be a server, a computer, or a processor or processing unit within a server or computer. The server may be a single server or a server cluster consisting of multiple servers. It should be noted that the embodiments of this application do not limit the specific device form of the data verification device 101. Figure 1 The data verification device 101 is used as an example of a single server.

[0051] In some embodiments, the terminal device may be a mobile phone, tablet computer, laptop computer, handheld computer, in-vehicle electronic device, mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device, personal computer (PC), ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc., and the embodiments of this application do not specifically limit it. Figure 1 The example shown is a mobile phone, with terminal device 102 as an example.

[0052] It should be noted that the network architecture described in the embodiments of this application is for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and does not constitute a limitation on the technical solutions provided in the embodiments of this application. As network architectures evolve, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0053] See Figure 2 This is a flowchart illustrating a data verification method provided in an embodiment of this application. Figure 2 As shown, the data verification method provided in this application embodiment can be implemented by the above-mentioned data verification device, specifically including the following steps 201 to 203.

[0054] Step 201: The data verification device acquires at least one first AIGC generated by a generating device.

[0055] In some embodiments, the data type of the first AIGC mentioned above includes at least one of the following: text, image, and video. Of course, the data type of the first AIGC may also include other data types, which can be determined according to actual needs, and this application does not limit this.

[0056] In some embodiments, the generation device includes at least one of the following: a generation model training device, a generation model invocation device, and a generation content post-processing device. The generation model training device is used to train the generation model required for generating the first AIGC; the generation model invocation device is used to invoke the generation model required for generating the first AIGC; and the generation content post-processing device is used to process the content generated by the generation model to obtain the first AIGC.

[0057] In some embodiments, the data verification device can obtain the first AIGC via the Internet. Of course, the data verification device can also obtain the first AIGC through other means, such as Bluetooth technology; to avoid redundancy, these will not be listed here.

[0058] Step 202: The data verification device obtains the metadata information corresponding to the first AIGC. The metadata information includes: the generation device identity information and the flow path log corresponding to the first AIGC.

[0059] In some embodiments, the generated device identity information includes at least one of the following: hardware root of trust information for each generated device, and a joint identity identifier for the at least one generated device.

[0060] In some embodiments, the aforementioned hardware root of trust information may also be referred to as PUF identity.

[0061] In some embodiments, the aforementioned hardware root of trust information is used to characterize the hardware information of the chip of the generating device, as well as the computing power characteristic information of the generating device when generating the first AIGC.

[0062] In some embodiments, the aforementioned joint identity may also be referred to as joint signature.

[0063] In some embodiments, the aforementioned joint identity identifier represents the device identity information of each of the at least one generating device.

[0064] In some embodiments, the computing power characteristic information described above represents the hardware physical parameters of the generating device when generating the first AIGC.

[0065] In some embodiments, the aforementioned computing power characteristic information includes at least one of the following: device computation latency information, device power consumption information, and device temperature information. Of course, the aforementioned computing power characteristic information may also include other information capable of characterizing the hardware physical information of the generating device when generating the first AICG, which can be determined according to actual needs, and this application does not limit this.

[0066] In some embodiments, the aforementioned device computation latency information refers to the time span consumed by the generating device in performing a specific computational task during the generation of the first AIGC. In AIGC generation scenarios, this typically involves complex neural network inference computations, such as large-scale matrix multiplication, convolution operations, or attention mechanisms. This information precisely records the time difference from the issuance of the input instruction to the generation of the final output result. Due to objective differences in the computing power and processing efficiency of different hardware architectures, computation latency exhibits significant device specificity. The verification device can use this as a performance fingerprint, comparing the actual latency data with the standard benchmark value for this type of device under a specific model to identify the authenticity of the device's identity. If the latency data deviates significantly from the expected range, it may indicate that the device identity has been forged or the generation process did not actually occur, thus providing a crucial time dimension for verifying the credibility of the AIGC's source.

[0067] In some embodiments, the aforementioned device power consumption information refers to the electrical energy consumed by the generating device during computation throughout the entire cycle of the first AIGC generation. The inference process of generative AI models is often accompanied by high-intensity parallel computing, which can cause significant fluctuations in hardware power over a short period. This information includes not only the total energy consumption value but also a dynamic curve showing how power consumption changes over time. Due to differences in the circuit design, manufacturing process, and energy efficiency of physical hardware, specific devices exhibit unique power consumption fingerprints when running specific models. By analyzing power consumption characteristics, the verification device can determine whether the generation process conforms to the operating rules of the physical hardware. For example, if the metadata claims to be generated by a high-performance graphics card, but the power consumption data remains at an extremely low level, it can be determined to be virtual machine simulation or forgery. Therefore, power consumption information is an important feature for distinguishing between real physical devices and software simulation environments, and for verifying the physical authenticity of the generation process.

[0068] In some embodiments, the aforementioned device temperature information refers to the temperature change data of the core computing unit (such as a GPU or central processing unit chip) of the generating device during the generation of the first AIGC. According to the laws of physical thermodynamics, high-load computing tasks lead to an increase in the current-induced thermal effect, which in turn causes the device temperature to rise. This information reflects the thermodynamic characteristics of the generation process and records the temperature fluctuation trajectory before, during, and after the computing task begins. When a physical device processes a complex AIGC task, it will inevitably be accompanied by a temperature rise curve that conforms to the characteristics of the heat dissipation system.

[0069] In this way, the data verification device binds the unique hardware fingerprint generated by PUF technology with computing power characteristics such as device computational latency, power consumption, and temperature when the device generates the first AIGC, thus obtaining the hardware root of trust corresponding to the first AIGC. This ensures that each AIGC generated by the generating device corresponds to unique hardware identity information. The AIGC carries a verifiable source identity from the moment it is generated, establishing a trustworthy foundation for subsequent tracing and verification. This effectively prevents the risk that the generating device corresponding to the first AIGC is a forged device, and improves the security of data verification of AIGC.

[0070] In some embodiments, after the generating device generates the first AIGC, the data verification device will write the first AIGC and its metadata information (including PUF hardware identity, joint signature, and flow log) into the blockchain distributed evidence storage network in real time.

[0071] In some embodiments, the metadata information written into the blockchain distributed evidence storage network can be referred to as evidence storage data or evidence storage information.

[0072] In some embodiments, the aforementioned blockchain distributed evidence storage network includes at least one blockchain node.

[0073] In some embodiments, blockchain nodes in the aforementioned distributed blockchain evidence storage network record the evidence data using a consensus algorithm, ensuring that once the data is on the chain, it is immutable and non-repudiable, and accessible and verifiable by all nodes in the network. This evidence storage mechanism achieves full lifecycle protection for the first AICG: from generation, transmission, distribution to use, every transfer operation can be recorded by the blockchain and bound to a timestamp. When the first AICG is transmitted across devices or distributed across networks, the evidence information is carried along with the content, forming a complete and continuous traceability chain, thereby preventing the data of the first AICG from being tampered with or forged during the transfer process.

[0074] In some embodiments, the data verification device uses the corresponding blockchain account address to preload the contract logic of content and evidence storage information on the blockchain node, thereby establishing an integrated mapping relationship from hardware identity to on-chain evidence storage, and jointly completing the construction of a trusted basic environment at the device layer, cryptographic layer and blockchain layer.

[0075] It should be noted that in the data verification method provided in this application, AIGc and device identity information are written together into the blockchain distributed evidence storage network at the blockchain evidence storage layer. The hardware identity chain, computing power binding information and content flow log are formed into a three-dimensional binding, which ensures that the on-chain data can not only verify whether the data has been tampered with, but also trace back to the specific physical device and computing power environment to verify whether the device is a counterfeit device, thus establishing a complete closed loop of "PUF identity → certificateless authentication → joint signature → blockchain traceability".

[0076] In some embodiments, when the above-mentioned at least one generating device includes only one generating device, after the generating device generates the first AICG using computing resources such as a graphics processing unit (GPU), the data verification device will strongly bind the first AICG to the PUF identity (also known as the hardware root of trust) of the generating device.

[0077] In some embodiments, when the at least one generating device includes multiple generating devices (i.e. multiple devices cooperating in the task), the data verification device first completes the authentication process of each generating device through a certificateless cryptographic system (also known as a certificateless cryptographic method or certificateless authentication system), and then generates a joint signature (also known as a joint identity identifier) ​​during the collaborative computation process of each generating device, thereby writing the identity endorsement and participation information of multiple parties into the metadata information of the first AIGC.

[0078] It should be noted that, for the case where at least one generating device includes multiple generating devices, the data verification device first completes the authentication process of each generating device through a certificateless cryptographic system, and then generates a joint signature during the collaborative computation process of each generating device. For the specific implementation process, please refer to the relevant description in the following embodiments. To avoid repetition, this application will not elaborate further here.

[0079] Step 203: The data verification device verifies the first AIGC based on the aforementioned metadata information.

[0080] In some embodiments, the above data verification includes: verifying whether the first AIGC has been tampered with and verifying whether the generating device corresponding to the first AIGC is a trusted device.

[0081] In some embodiments, verifying whether the first AIGC has been tampered with refers to verifying whether the first AIGC has been abnormally modified in the data flow path.

[0082] It should be noted that the specific implementation process for verifying whether the first AIGC of the electronic device has been abnormally modified in the data flow path can be found in the relevant description in the following embodiments. To avoid repetition, this application will not elaborate on it here.

[0083] In some embodiments, verifying whether the generating device corresponding to the first AIGC is a trusted device means verifying whether the generating device indicated in the metadata information of the first AIGC is consistent with the generating device indicated in the evidence storage information corresponding to the first AIGC stored in the blockchain distributed evidence storage network.

[0084] It should be noted that the specific implementation process for verifying whether the generating device indicated in the metadata information of the first AIGC is consistent with the generating device indicated in the evidence storage information corresponding to the first AIGC stored in the above-mentioned blockchain distributed evidence storage network can be found in the relevant description in the following embodiments. To avoid repetition, this application will not elaborate further here.

[0085] In some embodiments, data verification of the first AIGC can specifically include three aspects: First, by verifying the PUF identity (also known as the hardware root of trust) in the metadata information of the first AIGC, it is confirmed whether the source of the generating device is genuine and trustworthy (i.e., whether the generating device corresponding to the first AIGC is a counterfeit device), preventing counterfeit devices from impersonating the user; Second, by verifying the joint signature (also known as the joint identity identifier) ​​in the metadata information of the first AIGC, it is confirmed whether each device participated in the collaborative generation process of the first AIGC, ensuring that the first AIGC is indeed endorsed by multiple parties rather than being forged at a single point; Third, by verifying the circulation path information recorded in the blockchain distributed evidence storage network (also known as the circulation path log), it is confirmed whether the circulation path of the first AIGC is transparent and trustworthy, thereby verifying whether the first AIGC has been tampered with during the data circulation process. Through the above three aspects of verification, the data source and responsible party of the first AIGC can be clearly defined, thus achieving the security goal of "verifiable, traceable, and non-repudiable".

[0086] In some embodiments, combined with Figure 2 ,like Figure 3 As shown, step 203 above can be implemented through steps 203a and 203b.

[0087] Step 203a: The data verification device verifies whether the generating device corresponding to the first AIGC is a trusted device based on the aforementioned generating device identity information.

[0088] In some embodiments, when the at least one generating device is a single device, the data verification device can verify whether the generating device corresponding to the first AIGC is a trusted device based on the hardware trusted root information of the single generating device.

[0089] In some embodiments, when the at least one generating device is multiple devices, the data verification device can verify whether the generating device corresponding to the first AIGC is a trusted device based on the aforementioned joint identity identifier.

[0090] It is understandable that the data verification device can confirm the authenticity and trustworthiness of the PUF identity (i.e., hardware trusted root information or joint identity identifier) ​​of the source device based on the metadata of the first AIGC. When the metadata information of the first AIGC contains the joint identity identifier, the device can verify the authenticity and consistency of the process of multiple generating devices collaboratively generating the first AIGC by verifying the joint identity identifier, and detect whether there are counterfeit devices among the multiple generating devices, thereby improving the security of data verification of AIGC.

[0091] In some embodiments, the data verification device parses the metadata information of the first AIGC and verifies the authenticity and trustworthiness of the source hardware identity by verifying the PUF identity (i.e., hardware root of trust information) of the source device.

[0092] In some embodiments, the data verification device parses the metadata information of the first AIGC and verifies the authenticity and consistency of the collaborative creation process among multiple generating devices by verifying the joint identity (i.e., joint signature) of the source device. This mechanism can effectively identify and detect whether there are counterfeit devices among the participating devices, thereby preventing the infiltration of fake generation sources.

[0093] Step 203b: The data verification device verifies whether the first AIGC has been tampered with based on the flow path log corresponding to the first AIGC.

[0094] In some embodiments, the data verification device can obtain the data flow log corresponding to the first AIGC through the aforementioned blockchain distributed evidence storage network.

[0095] In some embodiments, the aforementioned flow path log may also be referred to as flow path information.

[0096] In some embodiments, the aforementioned flow path log represents the data transmission path information and data modification operation information during the data transmission process (such as cross-device transmission) of the first AIGC.

[0097] In some embodiments, the data verification device achieves accurate verification of data integrity by acquiring and parsing the flow path log corresponding to the first AIGC. This flow path log records the entire lifecycle of the AIGC from generation and transmission to storage. Based on the log content, the verification device tracks the operation records of each flow node one by one, focusing on verifying the consistency and continuity of the digital signatures, timestamps, and content hash values ​​at each stage.

[0098] Specifically, during the verification process, the data verification device compares the current AIGC data characteristics with the historical characteristics recorded in the logs. If the transfer chain is complete, the hash value has not mutated, and the signature verification passes, the data is confirmed to have not been tampered with; conversely, if log breaks, hash mismatches, or signature invalidations are found, it is determined that the data has been maliciously tampered with during the transfer process. This mechanism ensures the originality and integrity of AIGC content by constructing an immutable traceability chain, effectively improving the data verification system's resistance to attacks and its credibility.

[0099] It should be noted that step 203a can be performed before step 203b; or step 203a can be performed after step 203b; or step 203a can be performed simultaneously with step 203b.

[0100] In this way, the data verification device can obtain a complete lifecycle record of the first AIGC from generation to transmission based on the metadata information of the first AIGC, and verify whether the data of the first AIGC has been tampered with during the process of content transmission across devices or distribution across networks, thereby improving the security of data verification of AIGC.

[0101] The data verification method provided in this application binds the AIGC to the hardware information of the generating device and the AIGC's transfer path information by writing them into the AIGC's metadata. This allows the device to directly obtain the hardware information of the generating device and the transfer path information of the AIGC based on its metadata after acquiring the AIGC. Then, based on this hardware information and transfer path information, it can verify whether the AIGC has been tampered with or whether the generating device is a counterfeit device. Thus, based on verifying whether the AIGC has been tampered with, it can also verify whether the generating device is counterfeit by using the hardware information of the generating device carried in the AIGC's metadata. This solves the problem of not being able to identify whether the generating device corresponding to the AIGC is counterfeit, thereby improving the security of AIGC data verification.

[0102] In some embodiments, combined with Figure 2 ,like Figure 4 As shown, prior to step 202 above, the data verification method provided in this application embodiment may further include the following steps 302a and 302b.

[0103] Step 302a: The data verification device generates a unique hardware fingerprint for each generating device using PUF technology.

[0104] In some embodiments, the unique hardware fingerprint characterization generation device contains the hardware information of its chip.

[0105] In some embodiments, the above-mentioned PUF technology includes at least one of the following: PUF software development kit (SDK) technology, PUF intellectual property core (IP core) technology, and PUF chip technology.

[0106] In some embodiments, the software development kit (SDK) in the PUF SDK technology provides the implementation code and interfaces for PUF technology, and the production device can integrate the code or interfaces locally as needed. Therefore, the PUF SDK technology is suitable for production devices that wish to quickly acquire PUF capabilities at a lower cost through software upgrades.

[0107] In some embodiments, PUF IP core technology is an integrated circuit design technology suitable for manufacturing devices that wish to acquire PUF capabilities by embedding PUF circuit modules. A PUF circuit module is a dedicated chip integrating PUF technology, suitable for scenarios where PUF capabilities are desired through the addition of an external, independent chip.

[0108] In some embodiments, where at least one generating device includes multiple generating devices (such as a generating model training device, a generating model calling device, a generating content post-processing device, etc.), each generating device may arbitrarily select one of PUF SDK technology, PUF IP technology, and PUF chip technology to generate its corresponding unique hardware fingerprint.

[0109] In some embodiments, the generating device can register the unique hardware fingerprint generated by PUF technology in its local secure area, and simultaneously complete the registration at the management or control end, in order to determine the authenticity and uniqueness of the unique hardware fingerprint.

[0110] Step 302b: The data verification device generates the hardware root of trust information for each generating device based on the unique hardware fingerprint of each generating device and the computing power characteristic information of each generating device when generating the first AIGC.

[0111] In some embodiments, the data verification device can perform information fusion processing on the unique hardware fingerprint of each generating device and the computing power characteristic information of each generating device when generating the first AIGC, so as to generate the hardware trusted root information of each generating device.

[0112] It should be noted that the execution order of steps 302a and 302b described above is not limited in this embodiment. For example, step 201 can be executed first, followed by steps 302a and 302b; or, steps 302a and 302b can be executed first, followed by step 201; or, steps 201, 302a, and 302b can be executed simultaneously. Figure 4 This is illustrated by taking the example of executing step 201 first, followed by steps 302a and 302b.

[0113] In this way, the unique hardware fingerprint generated by PUF technology is bound to the computing power feature information when the device generates the first AIGC, thus obtaining the hardware root of trust corresponding to the first AIGC. This ensures that each AIGC generated by the generating device corresponds to unique hardware identity information. The AIGC carries a verifiable source identity from the moment it is generated, establishing a trustworthy foundation for subsequent tracing and verification. This effectively prevents the risk that the generating device corresponding to the first AIGC is a forged device, and improves the security of data verification of AIGC.

[0114] In some embodiments, the at least one generating device described above includes N generating devices, where N is an integer greater than 1. Exemplarily, in combination with... Figure 2 ,like Figure 5 As shown, prior to step 202 above, the data verification method provided in this application embodiment may further include the following steps 302c and 302d.

[0115] Step 302c: The data verification device performs authentication processing on the above N generating devices through a certificateless password authentication method.

[0116] In some embodiments, the above-described certificateless password authentication method may also be referred to as a certificateless password authentication system or a certificateless authentication system.

[0117] In some embodiments, the authentication process described above refers to enabling the N generating devices to authenticate each other in order to prevent counterfeit devices from impersonating generating devices to participate in the generation of joint identity identifiers.

[0118] Step 302d: After successfully authenticating the above N generating devices, the data verification device generates a joint identity identifier for the above N generating devices based on the hardware trusted root information of each generating device.

[0119] In some embodiments, if the data verification device fails to authenticate the above N generating devices, it indicates that there is a counterfeit device among the above N generating devices.

[0120] In some embodiments, the data verification device may, based on a certificateless cryptographic authentication system, assign lightweight public-private key pairs to the aforementioned N generating devices, and bind the hardware root of trust information of each generating device to its corresponding public-private key pair to obtain the identity identifier of each generating device. Based on the identity identifier of each generating device, a distributed federated computing mechanism is used to generate a joint identity identifier for the aforementioned N generating devices.

[0121] It is understandable that in multi-device collaborative generation scenarios, the data verification method provided in this application generates a unique hardware fingerprint for each generating device through PUF technology, and then combines it with a lightweight certificateless cryptographic system to achieve cross-device authentication and obtain the joint signature (i.e., the aforementioned joint identity identifier) ​​corresponding to the generating device. Unlike traditional PKI which requires central endorsement, the data verification method provided in this application directly authenticates each device through a certificateless cryptographic system, thereby omitting the certificate application and management process. Furthermore, the joint signature not only represents multi-party collaboration but also inherits the hardware non-cloning characteristic of the unique hardware fingerprint, enabling the joint signature to have hardware-level traceability capabilities. This allows for direct verification of the trustworthiness of the generating device corresponding to the first AIGC when the first AIGC flows across multiple devices through the joint signature.

[0122] It should be noted that the execution order of steps 302c and 302d described above is not limited in this embodiment. For example, step 201 can be executed first, followed by steps 302c and 302d; or, steps 302c and 302d can be executed first, followed by step 201; or, steps 201, 302c, and 302d can be executed simultaneously. Figure 5 This is illustrated by taking the example of executing step 201 first, followed by steps 302c and 302d.

[0123] Thus, by adopting a lightweight, certificate-free cryptographic authentication method, reliance on centralized certificates in traditional PKI is avoided, significantly reducing the complexity and computational overhead of the authentication process. Based on this, each generating device generates a joint identity identifier through a distributed, federated computing mechanism. This joint identity identifier not only serves as a joint endorsement by multiple parties but also records the identity information of multiple generating devices in the metadata of the first AIGC. This allows AIGC data verification to move beyond single-point authentication and instead perform multi-node collaborative consistency verification, ensuring that it can be verified whether there are counterfeit devices among the multiple generating devices corresponding to the first AIGC, thereby improving the security of AIGC data verification.

[0124] The data verification method of this application will be described below through specific embodiments.

[0125] like Figure 6As shown, the implementation process of the data verification method provided in this application embodiment includes the following steps:

[0126] Generation equipment: PUF technology + GPU chip

[0127] PUF technology: When generating the first AIGC, the data verification device uses PUF technology to generate a unique hardware fingerprint corresponding to the generating device.

[0128] GPU chip: The data verification device generates the hardware identity identifier (i.e., hardware root of trust) of the generating device based on the unique hardware fingerprint of the generating device and the computing power characteristic information of the GPU chip and other computing power resource chips when the generating device generates the first AIGC.

[0129] Therefore, the data verification device can obtain the generated content and hardware identity identifier.

[0130] Multi-device collaboration process: When the first AIGC is generated collaboratively by multiple devices, the data verification device performs authentication processing on each generating device based on a certificateless authentication system.

[0131] Joint signature calculation unit: When authentication is successful, the data verification device generates a joint signature (i.e., joint identity identifier) ​​corresponding to the generating device based on the hardware identity identifier corresponding to each device through the joint signature calculation unit.

[0132] Therefore, the data verification device can obtain the generated content and hardware identity identifier.

[0133] Distributed Evidence Storage: The data verification device performs distributed evidence storage on the first AIGC and its corresponding metadata information, recording the first AIGC and its corresponding metadata information on the blockchain network (i.e., metadata on-chain), as evidence storage information corresponding to the first AIGC. The metadata information corresponding to the first AIGC includes: the identity information of the generating device and the flow record log. The identity information of the generating device includes at least one of the following: the hardware root of trust information of each generating device, and the joint identity identifier of the aforementioned at least one generating device.

[0134] Therefore, the data verification device can obtain the generated content and evidence information.

[0135] Verification and Traceability: The data verification device verifies the device identity of the generating device corresponding to the first AIGC based on the metadata information of the first AIGC and the evidence storage information corresponding to the first AIGC in the blockchain network, in order to verify whether the generating device corresponding to the first AIGC is a counterfeit device. Furthermore, based on the aforementioned evidence storage information, the data verification device obtains the circulation path of the first AIGC and traces back to the original GPU based on this circulation path to verify whether the first AIGC has been tampered with during the circulation path.

[0136] Thus, the data verification method provided in this application uses PUF technology to map the unclonable physical characteristics of the generating device into a hardware identity fingerprint in real time, and binds it with the computing power characteristic information of the generating device when generating the first AIGC, forming the non-transferable and non-copyable hardware root of trust information corresponding to the first AIGC. In other words, the data verification method provided in this application proposes a two-factor binding method of "PUF hardware fingerprint + computing power characteristic", which deeply couples hardware identity with computing power characteristics, so that the identity information of the generating device corresponding to the first AIGC includes not only the physical hardware information of the device, but also the computing power execution environment information. This allows for dual verification of the generating device corresponding to the first AIGC at both the physical and computing power layers, and for the first time realizes a scheme for verifying AIGC based on computing power-level hardware root of trust information.

[0137] It should be noted that the descriptions of each step S1 to S5 in this embodiment can be found in the descriptions in the above embodiments, and will not be repeated here.

[0138] It should be noted that the above-described method embodiments, or the various possible implementations of the method embodiments, can be executed individually, or, provided there is no conflict, they can be combined with each other. The specific implementation can be determined according to actual usage requirements, and this application embodiment does not impose any restrictions on this.

[0139] As can be seen, the above mainly describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, the embodiments of this application provide corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the modules and algorithm steps of the various examples described in the embodiments disclosed herein, the embodiments of this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0140] This application embodiment can divide the data verification device into functional modules according to the above method example. For example, each function can be divided into its own functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. Optionally, the module division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0141] In some embodiments, this application also provides a data verification apparatus. The data verification apparatus may include one or more functional modules for implementing the data verification method of the above method embodiments.

[0142] For example, Figure 7 This is a schematic diagram of a data verification device provided in an embodiment of this application. Figure 7 As shown, the data verification device 900 includes an acquisition module 901 and a verification module 902.

[0143] The acquisition module 901 is used to acquire first AIGC (Artificial Intelligence Generated Content) generated by at least one generating device; and to acquire metadata information corresponding to the first AIGC. The metadata information includes the identity information of the generating device and the flow path log corresponding to the first AIGC. The identity information of the generating device includes at least one of the following: hardware root of trust information for each generating device, and a joint identity identifier for the at least one generating device. The hardware root of trust information is used to characterize the hardware information of the chip of the generating device, and the computing power characteristic information of the generating device when generating the first AIGC. The verification module 902 is used to perform data verification on the first AIGC based on the metadata information. This data verification includes: verifying whether the first AIGC has been tampered with, and verifying whether the generating device corresponding to the first AIGC is a trusted device.

[0144] In some embodiments, the data verification device further includes: a generation module; the generation module is configured to generate a unique hardware fingerprint for each generation device using PUF technology, the unique hardware fingerprint representing the hardware information of the chip of the generation device; and to generate hardware root of trust information for each generation device based on the unique hardware fingerprint of each generation device and the computing power feature information of each generation device when generating the first AIGC.

[0145] In other embodiments, the data verification device further includes: a generation module; the at least one generation device includes N generation devices, where N is an integer greater than 1; the verification module 902 is further configured to perform authentication processing on the N generation devices through a certificateless password authentication method; the generation module is configured to generate a joint identity identifier for the N generation devices based on the hardware trusted root information of each generation device after the verification module 902 has successfully authenticated the N generation devices.

[0146] In some other embodiments, the verification module 902 is specifically used to verify whether the generating device corresponding to the first AIGC is a trusted device based on the aforementioned generating device identity information, and to verify whether the first AIGC has been tampered with based on the flow path log corresponding to the first AIGC.

[0147] In some other embodiments, the computing power characteristic information mentioned above includes at least one of the following: device computing latency information, device power consumption information, and device temperature information.

[0148] It should be noted that the data verification device can implement all the processes implemented in the above method embodiments and achieve the same beneficial effects. To avoid repetition, it will not be described again here.

[0149] In the case where the functions of the integrated modules described above are implemented in hardware, this application provides a possible structural schematic diagram of the electronic device involved in the above embodiments. For example... Figure 8 As shown, the electronic device 90 includes: a processor 92, a communication interface 93, and a bus 94. Optionally, the electronic device 90 may also include a memory 91.

[0150] Processor 92 may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 92 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 92 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0151] Communication interface 93 is used to connect with other devices via a communication network. This communication network can be Ethernet, wireless access network, wireless local area network (WLAN), etc.

[0152] The memory 91 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0153] As one possible implementation, the memory 91 can exist independently of the processor 92. The memory 91 can be connected to the processor 92 via a bus 94 and is used to store instructions or program code. When the processor 92 calls and executes the instructions or program code stored in the memory 91, it can implement the data verification method provided in the embodiments of this application.

[0154] In another possible implementation, memory 91 can also be integrated with processor 92.

[0155] Bus 94 can be an Extended Industry Standard Architecture (EISA) bus, etc. Bus 94 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 8 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0156] Through the above description of the implementation methods, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the service calling device can be divided into different functional modules to complete all or part of the functions described above.

[0157] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above data verification method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0158] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0159] This application also provides a readable storage medium storing a program or instructions that, when executed by a computer, implement the data verification method provided in the above embodiments. It is understood that all or part of the processes in the above method embodiments can be executed by computer instructions instructing related hardware; the readable storage medium can be any of the foregoing embodiments or memory; the readable storage medium can also be an external storage device of the service invocation device, such as a pluggable hard drive, Smart MediaCard (SMC), Secure Digital (SD) card, flash card, etc., equipped on the service invocation device. Further, the readable storage medium can include both internal storage units of the service invocation device and external storage devices. The readable storage medium is used to store the computer program and other programs and data required by the service invocation device. The readable storage medium can also be used to temporarily store data that has been output or will be output.

[0160] This application also provides a computer program product, which is stored in a storage medium and implements the data verification method provided in the above embodiments when the computer program product is executed by a computer.

[0161] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0162] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0163] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A data verification method, characterized in that, include: Acquire at least one AIGC (Artificial Intelligence Generated Content) generated by a generating device; Obtain metadata information corresponding to the first AIGC; wherein, the metadata information includes the generation device identity information and the flow path log corresponding to the first AIGC; the generation device identity information includes at least one of the following: hardware root of trust information of each generation device, and the joint identity identifier of the at least one generation device; the hardware root of trust information is used to characterize the hardware information of the chip of the generation device, and the computing power feature information of the generation device when generating the first AIGC; Based on the metadata information, the first AIGC is verified. The data verification includes: verifying whether the first AIGC has been tampered with and verifying whether the generating device corresponding to the first AIGC is a trusted device.

2. The method according to claim 1, characterized in that, The method further includes: Using Physically Unclonable File (PUF) technology, a unique hardware fingerprint is generated for each generating device, and the unique hardware fingerprint represents the hardware information of the chip of the generating device. Based on the unique hardware fingerprint of each generating device and the computing power characteristic information of each generating device when generating the first AIGC, the hardware trusted root information of each generating device is generated.

3. The method according to claim 1, characterized in that, The at least one generating device includes N generating devices, where N is an integer greater than 1; the method further includes: The N generating devices are authenticated using a certificate-free password authentication method. After successful authentication of the N generating devices, a joint identity identifier for the N generating devices is generated based on the hardware root of trust information of each generating device.

4. The method according to any one of claims 1 to 3, characterized in that, The data verification of the first AIGC based on the metadata information includes: Based on the identity information of the generating device, verify whether the generating device corresponding to the first AIGC is a trusted device; Based on the flow path logs corresponding to the first AIGC, verify whether the first AIGC has been tampered with.

5. The method according to claim 1, characterized in that, The computing power characteristic information includes at least one of the following: device computing latency information, device power consumption information, and device temperature information.

6. A data verification device, characterized in that, include: Acquisition module and verification module; The acquisition module is used to acquire first artificial intelligence generated content (AIGC) generated by at least one generating device; In addition, the metadata information corresponding to the first AIGC is obtained; wherein, the metadata information includes: the identity information of the generating device and the flow path log corresponding to the first AIGC; the identity information of the generating device includes at least one of the following: the hardware root of trust information of each generating device, the joint identity identifier of the at least one generating device; the hardware root of trust information is used to characterize the hardware information of the chip of the generating device, and the computing power characteristic information of the generating device when generating the first AIGC; The verification module is used to perform data verification on the first AIGC based on the metadata information. The data verification includes: verifying whether the first AIGC has been tampered with and verifying whether the generating device corresponding to the first AIGC is a trusted device.

7. The apparatus according to claim 6, characterized in that, The device further includes: a generation module; the generation module is configured to generate a unique hardware fingerprint for each generation device using physically unclonable PUF technology, the unique hardware fingerprint representing the hardware information of the chip of the generation device; and, based on the unique hardware fingerprint of each generation device and the computing power feature information of each generation device when generating the first AIGC, generate hardware root of trust information for each generation device.

8. The apparatus according to claim 6, characterized in that, The apparatus further includes: a generation module; the at least one generation device includes N generation devices, where N is an integer greater than 1; the verification module is further configured to authenticate the N generation devices using a certificateless password authentication method; the generation module is configured to generate a joint identity identifier for the N generation devices based on the hardware root of trust information of each generation device after the generation module has successfully authenticated the N generation devices.

9. The apparatus according to any one of claims 6 to 8, characterized in that, The verification module is specifically used to verify whether the generating device corresponding to the first AIGC is a trusted device based on the identity information of the generating device, and to verify whether the first AIGC has been tampered with based on the flow path log corresponding to the first AIGC.

10. The apparatus according to claim 7, characterized in that, The computing power characteristic information includes at least one of the following: device computing latency information, device power consumption information, and device temperature information.

11. An electronic device, characterized in that, It includes a processor and a memory, the memory storing a program or instructions that can run on the processor, the program or instructions being executed by the processor to implement the data verification method as described in any one of claims 1-6.

12. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a computer, implement the data verification method as described in any one of claims 1-6.

13. A computer program product, characterized in that, The computer program product is stored in a storage medium, and when executed by a computer, the computer program product implements the data verification method as described in any one of claims 1-6.

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