pith. sign in

arxiv: 2406.14966 · v3 · submitted 2024-06-21 · 💻 cs.CY · cs.CR

Towards trustworthy management of AIGC copyright: blockchain-enabled full lifecycle recording and multi-party auditing approach

Jiajia Jiang , Moting Su , Fengshu Li , Xiangli Xiao , Yushu Zhang This is my paper

Pith reviewed 2026-05-24 00:28 UTC · model grok-4.3

classification 💻 cs.CY cs.CR
keywords AIGC copyrightblockchainlifecycle recordingmulti-party auditingAI-generated contentcopyright ownershipdecentralized storage
0
0 comments X

The pith

A blockchain system records every stage of AI content creation to let auditors determine copyright ownership.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes AIGC-Chain to solve copyright problems created by AI-generated content. Existing legal rules assume human creators, but AI now handles most of the work, making it hard to decide who owns the output or how to split profits. The system stores intermediate data from the entire creation process on a decentralized blockchain instead of only the final product. Auditors can then pull records to settle disputes and confirm ownership. Analyses in the paper indicate the approach meets performance and security needs for this task.

Core claim

AIGC-Chain conducts a comprehensive recording of intermediate data generated across the full lifecycle of AIGC. Such data is deposited into a decentralized blockchain for secure multi-party auditing, thereby constructing a trustworthy management for AIGC copyright. In copyright dispute scenarios, auditors can retrieve critical proof from the blockchain, facilitating precise determination of the copyright ownership of AIGC products.

What carries the argument

AIGC-Chain, a blockchain system that records and stores full-lifecycle intermediate contributions for multi-party auditing.

Load-bearing premise

All parties in AIGC generation will accurately and honestly record their intermediate contributions on the blockchain.

What would settle it

A test case in which one party omits or alters a contribution record yet the auditing process still treats the blockchain data as complete and authoritative proof of ownership.

Figures

Figures reproduced from arXiv: 2406.14966 by Fengshu Li, Jiajia Jiang, Moting Su, Xiangli Xiao, Yushu Zhang.

Figure 1
Figure 1. Figure 1: Graphical Abstract of AIGC-Chain tralized ledger to provide proof, thus offering a solid foun￾dation and support for resolving copyright conflicts. Specif￾ically, we maintain a world state with eight attributes for each AIGC product, ensuring that intermediate data of the generation process is securely stored on the blockchain in an immutable manner. This methodology establishes a robust framework ensuring… view at source ↗
Figure 2
Figure 2. Figure 2: The Structure of Consortium Blockchain Network [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The Technical Principle of Indistinguishable Bloom Filter [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: The Data Structure of the Transaction in Fabric [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Lifecycle Management on the Blockchain 1) Registration: Users participating in AIGC-Chain are required to register on the blockchain network, with digital certificates DC and public-private key pairs (pk, sk) being issued by the Certificate Authority (CA) on the chain. Only users who have completed CA authentication are granted access to view and participate in blockchain activities. Within this architectu… view at source ↗
Figure 7
Figure 7. Figure 7: Performance Analysis of AIGC-Chain 7.2 Blockchain Evaluation The cost efficiency of AIGC-Chain is evaluated based on the amount of gas required for each contract function. We analyze computational costs for various procedures within AIGC-Chain, meticulously documenting transaction costs and execution costs. Transaction gas represents the actual amount of gas expended, whereas execution gas refers to the ga… view at source ↗
Figure 8
Figure 8. Figure 8: Execution Efficiency of CTrace 7.3 Evaluation of CTrace This research conducts a comparative evaluation of CTrace and Fast Query [9], with a focus on their respective opti￾mization strategies for data retrieval. CTrace facilitates rapid transaction verification by leveraging a bloom filter within the world state in a systematic manner. In contrast, Fast Query optimizes search efficiency by using a single l… view at source ↗
Figure 9
Figure 9. Figure 9: Performance Analysis of CTrace 8 CONCLUSION This paper confronts the copyright challenges associated with AIGC and introduces a trustworthy AIGC copyright management system for AIGC. It meticulously records in￾termediate data throughout the full lifecycle of AIGC and anchors it within a decentralized blockchain system, thereby establishing a robust multi-party supervision framework for the fair and reliabl… view at source ↗
read the original abstract

With the escalating proliferation of artificial intelligence technologies, AI-generated content (AIGC) has progressively permeated across diverse domains. However, this explosive application has also sparked widespread public discussion about the copyright of AIGC. Existing copyright legal frameworks, originally designed around human creators, now face a paradigm shift. As human involvement in the generation of AIGC diminishes, where creative expression increasingly hinges on AI. This discrepancy has introduced multifaceted complexities and challenges in determining the copyright ownership of AIGC within established legal boundaries. Given this, meticulous recording and auditing of contributions from all parties in AIGC generation becomes imperative. Blockchain, with its decentralized storage, offers a robust technical foundation for AIGC copyright management. Yet existing blockchain-based solutions have clear limitations: most only focus on certifying final generated products, ignoring the management of critical intermediate data across the full lifecycle, thus failing to meet the needs of core scenarios like copyright confirmation and multi-party profit distribution. For this purpose, this paper introduces AIGC-Chain, a trustworthy AIGC copyright management system. It conducts a comprehensive recording of intermediate data generated across the full lifecycle of AIGC. Such data is deposited into a decentralized blockchain for secure multi-party auditing, thereby constructing a trustworthy management for AIGC copyright. In copyright dispute scenarios, auditors can retrieve critical proof from the blockchain, facilitating precise determination of the copyright ownership of AIGC products. Both theoretical and experimental analyses confirm that this scheme shows exceptional performance and security in AIGC copyright management.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper proposes AIGC-Chain, a blockchain-based system designed to record intermediate data across the full lifecycle of AI-generated content (AIGC) for secure multi-party auditing and copyright management. It argues that existing blockchain solutions are limited to final products and introduces this approach to enable precise ownership determination in disputes, claiming that both theoretical and experimental analyses confirm exceptional performance and security.

Significance. If the full-lifecycle recording and auditing mechanisms can be shown to function as described, the work addresses a timely gap in AIGC copyright handling by leveraging blockchain immutability for contribution tracking and profit distribution. The emphasis on intermediate data (prompts, model versions, training steps) is a potentially useful extension beyond final-product certification, though its practical impact depends on resolving input-integrity issues.

major comments (3)
  1. [Abstract, §1] Abstract and §1: The central claim that 'both theoretical and experimental analyses confirm that this scheme shows exceptional performance and security' is not supported by any methods, metrics, datasets, or analysis details in the provided text, preventing evaluation of the performance and security assertions.
  2. [Abstract, system description] The design assumes that all parties will accurately and honestly record every intermediate contribution on the blockchain (prompts, model versions, training steps). Blockchain provides post-recording immutability but no technical enforcement or verification for input completeness or truthfulness; if this assumption fails, the multi-party auditing process cannot reconstruct ownership or shares, undermining the copyright-determination use case.
  3. [System architecture] No description is given of how the system handles partial or adversarial recording (e.g., omitted steps, falsified prompts), which is load-bearing for the legal-determination scenario.
minor comments (2)
  1. Notation for data structures and auditing protocols should be formalized with explicit definitions and invariants.
  2. The paper should include a clear threat model specifying which parties are trusted and which are not.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major comment point by point below, indicating planned revisions where appropriate.

read point-by-point responses

  1. Referee: [Abstract, §1] Abstract and §1: The central claim that 'both theoretical and experimental analyses confirm that this scheme shows exceptional performance and security' is not supported by any methods, metrics, datasets, or analysis details in the provided text, preventing evaluation of the performance and security assertions.

    Authors: The full manuscript contains a security analysis (Section 4) with formal proofs and a performance evaluation (Section 5) reporting concrete metrics such as throughput, latency, and storage costs on specific test datasets. We agree the abstract and §1 should better support the claim by referencing these sections and summarizing key results. We will revise both to include brief descriptions of the methods and findings. revision: yes

  2. Referee: [Abstract, system description] The design assumes that all parties will accurately and honestly record every intermediate contribution on the blockchain (prompts, model versions, training steps). Blockchain provides post-recording immutability but no technical enforcement or verification for input completeness or truthfulness; if this assumption fails, the multi-party auditing process cannot reconstruct ownership or shares, undermining the copyright-determination use case.

    Authors: The system is designed under the assumption of honest recording by participants who have clear incentives to establish verifiable copyright claims. Blockchain immutability protects recorded data after submission, but we acknowledge the lack of built-in input verification. We will expand the system description to explicitly state this assumption and discuss potential mitigations such as multi-signature requirements or oracle-based checks. revision: partial

  3. Referee: [System architecture] No description is given of how the system handles partial or adversarial recording (e.g., omitted steps, falsified prompts), which is load-bearing for the legal-determination scenario.

    Authors: The architecture focuses on the recording and auditing workflow assuming complete participation. We agree that robustness to partial or adversarial inputs requires additional treatment. We will add a dedicated subsection on handling incomplete records via cross-party consistency verification and note limitations where external legal mechanisms may be needed to address falsification. revision: yes

Circularity Check

0 steps flagged

No significant circularity in system design proposal

full rationale

The paper proposes AIGC-Chain, a blockchain-based system for full-lifecycle AIGC data recording and multi-party auditing. No mathematical derivations, equations, fitted parameters, or predictions appear in the abstract or described structure. Claims of performance and security rest on separate theoretical and experimental analyses rather than reducing to self-definitions, self-citations, or input-renaming. The central design assumption of honest recording is an external premise, not a circular step within any derivation chain. The work is self-contained as an engineering proposal.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

The central claim rests on the introduction of the AIGC-Chain architecture itself; no free parameters, standard axioms, or independently evidenced invented entities are described in the abstract.

invented entities (1)
  • AIGC-Chain no independent evidence
    purpose: Blockchain system for full-lifecycle AIGC data recording and multi-party auditing
    Introduced as the core new system in the abstract; no independent evidence provided.

pith-pipeline@v0.9.0 · 5821 in / 1124 out tokens · 23688 ms · 2026-05-24T00:28:37.541904+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

48 extracted references · 48 canonical work pages · 1 internal anchor

  1. [1]

    Combating prerelease piracy: Model- ing the effects of antipiracy measures in p2p networks,

    H. Il-Horn and O. JooHee, “Combating prerelease piracy: Model- ing the effects of antipiracy measures in p2p networks,”INFORMS Journal on Computing, vol. 29, no. 1, pp. 92–107, 2016

    work page 2016

  2. [2]

    A deep learning and image processing pipeline for object characterization in firm operations,

    A. Alireza, R. Arun, and X. Yusen, “A deep learning and image processing pipeline for object characterization in firm operations,” INFORMS Journal on Computing, vol. 36, no. 2, pp. 616–634, 2023

    work page 2023

  3. [3]

    Economics of permissioned blockchain adoption,

    G. Iyengar, F. Saleh, J. Sethuraman, and W. Wang, “Economics of permissioned blockchain adoption,” Management Science, vol. 69, no. 6, pp. 3415–3436, 2023

    work page 2023

  4. [4]

    The (limited) power of blockchain networks for information provision,

    B. Franke, Q. G. Fritz, and A. Stenzel, “The (limited) power of blockchain networks for information provision,” Management Science, vol. 70, no. 2, pp. 971–990, 2024

    work page 2024

  5. [5]

    Ad- vances in blockchain and crypto economics,

    B. Biais, A. Capponi, L. W. Cong, V . Gaur, and K. Giesecke, “Ad- vances in blockchain and crypto economics,” Management Science, vol. 69, no. 11, pp. 6417–6426, 2023

    work page 2023

  6. [6]

    Recording behaviors of artificial intelligence in blockchains,

    Y. Zhang, J. Zhao, J. Jiang, Y. Zhu, L. Wang, and Y. Xiang, “Recording behaviors of artificial intelligence in blockchains,” IEEE Transactions on Artificial Intelligence , vol. 4, no. 6, pp. 1437– 1448, 2023

    work page 2023

  7. [7]

    Blockchain-empowered lifecycle management for ai-generated content products in edge networks,

    Y. Liu, H. Du, D. Niyato, J. Kang, Z. Xiong, C. Miao, X. S. Shen, and A. Jamalipour, “Blockchain-empowered lifecycle management for ai-generated content products in edge networks,” IEEE Wireless Communications, pp. 1–9, 2024

    work page 2024

  8. [8]

    Circuit copyright blockchain: Blockchain-based homomorphic encryption for ip circuit protection,

    W. Liang, D. Zhang, X. Lei, M. Tang, K.-C. Li, and A. Y. Zomaya, “Circuit copyright blockchain: Blockchain-based homomorphic encryption for ip circuit protection,” IEEE Transactions on Emerging Topics in Computing, vol. 9, no. 3, pp. 1410–1420, 2021

    work page 2021

  9. [9]

    A blockchain-based copyright protection scheme with proactive de- fense,

    X. Chen, A. Yang, J. Weng, Y. Tong, C. Huang, and T. Li, “A blockchain-based copyright protection scheme with proactive de- fense,” IEEE Transactions on Services Computing , vol. 16, no. 4, pp. 2316–2329, 2023

    work page 2023

  10. [10]

    Fair outsourcing paid in fiat money using blockchain,

    X. Xiao, Y. Zhang, X. Dong, L. Wang, Y. Xiang, and X. Cao, “Fair outsourcing paid in fiat money using blockchain,” IEEE Transactions on Services Computing , vol. 16, no. 3, pp. 1860–1873, 2023

    work page 2023

  11. [11]

    Hades: Hash-based audio copy detection system for copyright protection in decentralized music sharing,

    M. R. R. Ansori, Allwinnaldo, R. N. Alief, I. S. Igboanusi, J. M. Lee, and D.-S. Kim, “Hades: Hash-based audio copy detection system for copyright protection in decentralized music sharing,” IEEE Transactions on Network and Service Management, vol. 20, no. 3, pp. 2845–2853, 2023

    work page 2023

  12. [12]

    Image copyright protection based on blockchain and zero-watermark,

    B. Wang, S. Jiawei, W. Wang, and P . Zhao, “Image copyright protection based on blockchain and zero-watermark,” IEEE Trans- actions on Network Science and Engineering , vol. 9, no. 4, pp. 2188– 2199, 2022

    work page 2022

  13. [13]

    Mc- dsc: A dynamic secure resource configuration scheme based on medical consortium blockchain,

    W. Liang, S. Xie, K.-C. Li, X. Li, X. Kui, and A. Y. Zomaya, “Mc- dsc: A dynamic secure resource configuration scheme based on medical consortium blockchain,” IEEE Transactions on Information Forensics and Security, vol. 19, pp. 3525–3538, 2024

    work page 2024

  14. [14]

    Theory and practice of bloom filters for distributed systems,

    S. Tarkoma, C. E. Rothenberg, and E. Lagerspetz, “Theory and practice of bloom filters for distributed systems,” IEEE Commun. Surveys Tutorials, vol. 14, no. 1, pp. 131–155, 2012

    work page 2012

  15. [15]

    Adaptively secure conjunctive query pro- cessing over encrypted data for cloud computing,

    R. Li and A. X. Liu, “Adaptively secure conjunctive query pro- cessing over encrypted data for cloud computing,” in 2017 IEEE 33rd International Conference on Data Engineering (ICDE) , 2017, pp. 697–708

    work page 2017

  16. [16]

    Privacy-preserving bloom filter-based keyword search over large encrypted cloud data,

    Y. Liang, J. Ma, Y. Miao, D. Kuang, X. Meng, and R. H. Deng, “Privacy-preserving bloom filter-based keyword search over large encrypted cloud data,” IEEE Transactions on Computers , vol. 72, no. 11, pp. 3086–3098, 2023

    work page 2023

  17. [17]

    Quick block trans- port system for scalable hyperledger fabric blockchain over d2d- assisted 5g networks,

    R. H. Kim, H. Noh, H. Song, and G. S. Park, “Quick block trans- port system for scalable hyperledger fabric blockchain over d2d- assisted 5g networks,” IEEE Transactions on Network and Service Management, vol. 19, no. 2, pp. 1176–1190, 2022

    work page 2022

  18. [18]

    The sentimental fools and the fictitious authors: rethinking the copyright issues of ai-generated contents in china,

    T. He, “The sentimental fools and the fictitious authors: rethinking the copyright issues of ai-generated contents in china,” Asia Pacific Law Review, vol. 27, no. 2, pp. 218–238, 2019

    work page 2019

  19. [19]

    Copyright protection for ai- generated works: Exploring originality and ownership in a digital landscape,

    H. GAFFAR and S. ALBARASHDI, “Copyright protection for ai- generated works: Exploring originality and ownership in a digital landscape,” Asian Journal of International Law, pp. 1–24, 2024

    work page 2024

  20. [20]

    Copyright protection for ai-generated outputs: The experience from china,

    Y. Wan and H. Lu, “Copyright protection for ai-generated outputs: The experience from china,” Computer Law & Security Review , vol. 42, p. 105581, 2021

    work page 2021

  21. [21]

    Systematic literature review: Blockchain security in nft ownership,

    R. A. A. Mochram, C. T. Makawowor, K. M. Tanujaya, J. V . Mo- niaga, and B. A. Jabar, “Systematic literature review: Blockchain security in nft ownership,” in 2022 International Conference on Electrical and Information Technology (IEIT), 2022, pp. 302–306

    work page 2022

  22. [22]

    Fingerchain: Copy- righted multi-owner media sharing by introducing asymmetric fingerprinting into blockchain,

    X. Xiao, Y. Zhang, Y. Zhu, P . Hu, and X. Cao, “Fingerchain: Copy- righted multi-owner media sharing by introducing asymmetric fingerprinting into blockchain,” IEEE Transactions on Network and Service Management, vol. 20, no. 3, pp. 2869–2885, 2023

    work page 2023

  23. [23]

    Blockchain-based audio watermarking technique for multimedia copyright protection in distribution networks,

    I. Natgunanathan, P . Praitheeshan, L. Gao, Y. Xiang, and L. Pan, “Blockchain-based audio watermarking technique for multimedia copyright protection in distribution networks,” ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 18, no. 3, pp. 1–23, 2022

    work page 2022

  24. [24]

    Copyright in the blockchain era: Promises and challenges,

    A. Savelyev, “Copyright in the blockchain era: Promises and challenges,” Computer Law & Security Review , vol. 34, no. 3, pp. 550–561, 2018

    work page 2018

  25. [25]

    Towards reliable utilization of aigc: Blockchain-empowered ownership ver- ification mechanism,

    C. Chen, Y. Li, Z. Wu, M. Xu, R. Wang, and Z. Zheng, “Towards reliable utilization of aigc: Blockchain-empowered ownership ver- ification mechanism,” IEEE Open Journal of the Computer Society , vol. 4, pp. 326–337, 2023

    work page 2023

  26. [26]

    Security and privacy on generative data in aigc: A survey,

    T. Wang, Y. Zhang, S. Qi, R. Zhao, Z. Xia, and J. Weng, “Security and privacy on generative data in aigc: A survey,” ACM Comput. Surv., vol. 57, no. 4, Dec. 2024

    work page 2024

  27. [27]

    Towards efficient consistency auditing of dynamic data in cross-chain inter- action,

    J. Jiang, Y. Zhang, J. Zhao, L. Gao, L. Zhu, and Z. Tian, “Towards efficient consistency auditing of dynamic data in cross-chain inter- action,” IEEE Transactions on Dependable and Secure Computing, pp. 1–13, 2025

    work page 2025

  28. [28]

    A comprehensive survey of ai-generated content (aigc): A history of generative ai from gan to chatgpt,

    Y. Cao, S. Li, Y. Liu, Z. Yan, Y. Dai, P . S. Yu, and L. Sun, “A comprehensive survey of ai-generated content (aigc): A history of generative ai from gan to chatgpt,” arXiv, 2023. [Online]. Available: https://arxiv.org/abs/2303.04226

    work page arXiv 2023

  29. [29]

    Design scheme of copyright management system based on digital wa- termarking and blockchain,

    Z. Meng, T. Morizumi, S. Miyata, and H. Kinoshita, “Design scheme of copyright management system based on digital wa- termarking and blockchain,” in 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), vol. 02, 2018, pp. 359–364

    work page 2018

  30. [30]

    Generative adversarial networks,

    M. Krichen, “Generative adversarial networks,” in 2023 14th In- ternational Conference on Computing Communication and Networking Technologies (ICCCNT), 2023, pp. 1–7

    work page 2023

  31. [31]

    DeepFakes: a New Threat to Face Recognition? Assessment and Detection

    P . Korshunov and S. Marcel, “Deepfakes: a new threat to face recognition? assessment and detection,” arXiv, 2018. [Online]. Available: https://arxiv.org/abs/1812.08685

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  32. [32]

    Artificial intelligence-generated and human expert-designed vocabulary tests: A comparative study,

    Y. Luo, W. Wei, and Z. Ying, “Artificial intelligence-generated and human expert-designed vocabulary tests: A comparative study,” Sage Open, vol. 12, no. 1, p. 21582440221082130, 2022

    work page 2022

  33. [33]

    Pricing game and blockchain for electricity data trading in low- carbon smart energy systems,

    Z. Liu, B. Huang, Y. Li, Q. Sun, T. B. Pedersen, and D. W. Gao, “Pricing game and blockchain for electricity data trading in low- carbon smart energy systems,” IEEE Transactions on Industrial Informatics, vol. 20, no. 4, pp. 6446–6456, 2024

    work page 2024

  34. [34]

    Dciv: Decentralized cross-chain data integrity verification with blockchain,

    J. Jiang, Y. Zhang, Y. Zhu, X. Dong, L. Wang, and Y. Xiang, “Dciv: Decentralized cross-chain data integrity verification with blockchain,” Journal of King Saud University - Computer and Infor- mation Sciences, vol. 34, no. 10, Part A, pp. 7988–7999, 2022

    work page 2022

  35. [35]

    Bedcv: Blockchain-enabled decentralized consistency verification for cross-chain calculation,

    Y. Zhang, J. Jiang, X. Dong, L. Wang, and Y. Xiang, “Bedcv: Blockchain-enabled decentralized consistency verification for cross-chain calculation,” IEEE Transactions on Cloud Computing , vol. 11, no. 3, pp. 2273–2284, 2023

    work page 2023

  36. [36]

    Travel with wander in the metaverse: An ai chatbot to visit the future earth,

    Y. Sun, Y. Xu, C. Cheng, Y. Li, C. H. Lee, and A. Asadipour, “Travel with wander in the metaverse: An ai chatbot to visit the future earth,” in 2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP), 2022, pp. 1–6

    work page 2022

  37. [37]

    Artificial intelligence and computer science in education: From kindergarten to university,

    M. Kandlhofer, G. Steinbauer, S. Hirschmugl-Gaisch, and P . Huber, “Artificial intelligence and computer science in education: From kindergarten to university,” in 2016 IEEE Frontiers in Education Conference (FIE), 2016, pp. 1–9

    work page 2016

  38. [38]

    Challenges and remedies to privacy and security in aigc: Exploring the potential of privacy computing, blockchain, and beyond,

    C. Chen, Z. Wu, Y. Lai, W. Ou, T. Liao, and Z. Zheng, “Challenges and remedies to privacy and security in aigc: Exploring the potential of privacy computing, blockchain, and beyond,” arXiv,

    work page

  39. [39]

    Available: https://arxiv.org/abs/2306.00419

    [Online]. Available: https://arxiv.org/abs/2306.00419

    work page arXiv

  40. [40]

    Cpchain: A copyright-preserving crowdsourcing data trading framework based on blockchain,

    D. Sheng, M. Xiao, A. Liu, X. Zou, B. An, and S. Zhang, “Cpchain: A copyright-preserving crowdsourcing data trading framework based on blockchain,” in 2020 29th International Conference on Computer Communications and Networks (ICCCN), 2020, pp. 1–9. IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, VOL. X, NO. X, XX 2025 13

    work page 2020

  41. [41]

    Blockchain-based p2p multimedia content distribution using collusion-resistant fingerprinting,

    A. Qureshi and D. Meg ´ıas, “Blockchain-based p2p multimedia content distribution using collusion-resistant fingerprinting,” in 2019 Asia-Pacific Signal and Information Processing Association An- nual Summit and Conference (APSIP A ASC), 2019, pp. 1606–1615

    work page 2019

  42. [42]

    Ai generated works and copyright protection,

    Y. Burylo, “Ai generated works and copyright protection,” En- trepreneurship, Economy and Law, 2022

    work page 2022

  43. [43]

    Secure spread spectrum watermarking for multimedia,

    I. Cox, J. Kilian, F. Leighton, and T. Shamoon, “Secure spread spectrum watermarking for multimedia,” IEEE Transactions on Image Processing, vol. 6, no. 12, pp. 1673–1687, 1997

    work page 1997

  44. [44]

    Katzenbeisser, A

    S. Katzenbeisser, A. Lemma, M. U. Celik, M. van der Veen, and M. Maas, “A buyer–seller watermarking protocol based on Jiajia Jiang received the B.S. degree in network engineering from the School of Information Sci- ence and Technology, Nanjing Agricultural Uni- versity, Nanjing, China, in June 2020. She is currently pursuing the M.S. degree with the Col- ...

    work page 2020

  45. [45]

    Lookup-table-based secure client-side embedding for spread- spectrum watermarks,

    M. U. Celik, A. N. Lemma, S. Katzenbeisser, and M. van der Veen, “Lookup-table-based secure client-side embedding for spread- spectrum watermarks,” IEEE Transactions on Information Forensics and Security, vol. 3, no. 3, pp. 475–487, 2008

    work page 2008

  46. [46]

    Secure and high- quality watermarking algorithms for relational database based on semantic,

    W. Li, N. Li, J. Yan, Z. Zhang, P . Yu, and G. Long, “Secure and high- quality watermarking algorithms for relational database based on semantic,” IEEE Transactions on Knowledge and Data Engineering , vol. 35, no. 7, pp. 7440–7456, 2023

    work page 2023

  47. [47]

    A robust database watermarking scheme that preserves statistical characteristics,

    Z. Ren, H. Fang, J. Zhang, Z. Ma, R. Lin, W. Zhang, and N. Yu, “A robust database watermarking scheme that preserves statistical characteristics,” IEEE Transactions on Knowledge and Data Engineer- ing, vol. 36, no. 6, pp. 2329–2342, 2024

    work page 2024

  48. [48]

    A robust, distortion min- imizing technique for watermarking relational databases using once-for-all usability constraints,

    M. Kamran, S. Suhail, and M. Farooq, “A robust, distortion min- imizing technique for watermarking relational databases using once-for-all usability constraints,” IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 12, pp. 2694–2707, 2013. Xiangli Xiao received the B.E. degree from the College of Electronic and Information Engineer- ing, Sou...

    work page 2013