Related papers: AIGC-Chain: A Blockchain-Enabled Full Lifecycle Recording System for AIGC Product Copyright Management

AIGC-Chain: A Blockchain-Enabled Full Lifecycle Recording System for AIGC Product Copyright Management

URL: http://arxiv.org/abs/2406.14966v1
Date: Fri, 21 Jun 2024 08:22:39 GMT
Title: AIGC-Chain: A Blockchain-Enabled Full Lifecycle Recording System for AIGC Product Copyright Management
Authors: Jiajia Jiang, Moting Su, Xiangli Xiao, Yushu Zhang, Yuming Fang,
Abstract summary: The current legal framework for copyright and intellectual property is grounded in the concept of human authorship. In the creation of AIGC, human creators provide conceptual ideas, with AI independently responsible for the expressive elements. It is imperative to reassess the intellectual contributions of all parties involved in the creation of AIGC to ensure a fair allocation of copyright ownership.
Score: 30.690595004607385
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: As artificial intelligence technology becomes increasingly prevalent, Artificial Intelligence Generated Content (AIGC) is being adopted across various sectors. Although AIGC is playing an increasingly significant role in business and culture, questions surrounding its copyright have sparked widespread debate. The current legal framework for copyright and intellectual property is grounded in the concept of human authorship, but in the creation of AIGC, human creators primarily provide conceptual ideas, with AI independently responsible for the expressive elements. This disconnect creates complexity and difficulty in determining copyright ownership under existing laws. Consequently, it is imperative to reassess the intellectual contributions of all parties involved in the creation of AIGC to ensure a fair allocation of copyright ownership. To address this challenge, we introduce AIGC-Chain, a blockchain-enabled full lifecycle recording system designed to manage the copyright of AIGC products. It is engineered to meticulously document the entire lifecycle of AIGC products, providing a transparent and dependable platform for copyright management. Furthermore, we propose a copyright tracing method based on an Indistinguishable Bloom Filter, named IBFT, which enhances the efficiency of blockchain transaction queries and significantly reduces the risk of fraudulent copyright claims for AIGC products. In this way, auditors can analyze the copyright of AIGC products by reviewing all relevant information retrieved from the blockchain.

Related papers

Using AI Alignment Theory to understand the potential pitfalls of regulatory frameworks [55.2480439325792]
This paper critically examines the European Union's Artificial Intelligence Act (EU AI Act) Uses insights from Alignment Theory (AT) research, which focuses on the potential pitfalls of technical alignment in Artificial Intelligence. As we apply these concepts to the EU AI Act, we uncover potential vulnerabilities and areas for improvement in the regulation.
arXiv Detail & Related papers (2024-10-10T17:38:38Z)
Do Responsible AI Artifacts Advance Stakeholder Goals? Four Key Barriers Perceived by Legal and Civil Stakeholders [59.17981603969404]
The responsible AI (RAI) community has introduced numerous processes and artifacts to facilitate transparency and support the governance of AI systems. We conduct semi-structured interviews with 19 government, legal, and civil society stakeholders who inform policy and advocacy around responsible AI efforts. We organize these beliefs into four barriers that help explain how RAI artifacts may (inadvertently) reconfigure power relations across civil society, government, and industry.
arXiv Detail & Related papers (2024-08-22T00:14:37Z)
©Plug-in Authorization for Human Content Copyright Protection in Text-to-Image Model [71.47762442337948]
State-of-the-art models create high-quality content without crediting original creators. We propose the copyright Plug-in Authorization framework, introducing three operations: addition, extraction, and combination. Extraction allows creators to reclaim copyright from infringing models, and combination enables users to merge different copyright plug-ins.
arXiv Detail & Related papers (2024-04-18T07:48:00Z)
Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues in Generative AI [2.669847575321326]
The survey aims to stay abreast of the latest developments and open problems. It will first outline methods of detecting copyright infringement in mediums such as text, image, and video. Next, it will delve an exploration of existing techniques aimed at safeguarding copyrighted works from generative models.
arXiv Detail & Related papers (2024-03-31T22:10:01Z)
Prompting the E-Brushes: Users as Authors in Generative AI [0.0]
The Copyright Office, in its March 2023 Guidance, argues against users of Generative AI being eligible for copyright protection. This Article challenges this viewpoint and advocates for the recognition of Generative AI users who incorporate these tools into their creative endeavors. Rather than dismissing the contributions generated by AI, this Article suggests a simplified and streamlined registration process.
arXiv Detail & Related papers (2024-03-25T02:20:14Z)
Generative AI and Copyright: A Dynamic Perspective [0.0]
generative AI is poised to disrupt the creative industry. The compensation to creators whose content has been used to train generative AI models (the fair use standard) and the eligibility of AI-generated content for copyright protection (AI-copyrightability) are key issues. This paper aims to better understand the economic implications of these two regulatory issues and their interactions.
arXiv Detail & Related papers (2024-02-27T07:12:48Z)
Copyleft for Alleviating AIGC Copyright Dilemma: What-if Analysis, Public Perception and Implications [4.959125079708047]
AIGC copyright dilemma can immensely stifle the development of AIGC and greatly cost the entire society. Previous work advocated copyleft on AI governance but without substantive analysis. Key findings include: a) people generally perceive the dilemma, b) they prefer to use authorized AIGC under loose restriction, and c) they are positive to copyleft in AIGC and willing to use it in the future.
arXiv Detail & Related papers (2024-02-19T15:20:35Z)
Copyright Protection in Generative AI: A Technical Perspective [58.84343394349887]
Generative AI has witnessed rapid advancement in recent years, expanding their capabilities to create synthesized content such as text, images, audio, and code. The high fidelity and authenticity of contents generated by these Deep Generative Models (DGMs) have sparked significant copyright concerns. This work delves into this issue by providing a comprehensive overview of copyright protection from a technical perspective.
arXiv Detail & Related papers (2024-02-04T04:00:33Z)
Training Is Everything: Artificial Intelligence, Copyright, and Fair Training [9.653656920225858]
Authors: Companies that use such content to train their AI engine often believe such usage should be considered "fair use" Authors: Copyright owners, as well as their supporters, consider the incorporation of copyrighted works into training sets for AI to constitute misappropriation of owners' intellectual property. We identify both strong and spurious arguments on both sides of this debate.
arXiv Detail & Related papers (2023-05-04T04:01:00Z)
A Pathway Towards Responsible AI Generated Content [68.13835802977125]
We focus on 8 main concerns that may hinder the healthy development and deployment of AIGC in practice. These concerns include risks from (1) privacy; (2) bias, toxicity, misinformation; (3) intellectual property (IP); (4) robustness; (5) open source and explanation; (6) technology abuse; (7) consent, credit, and compensation; (8) environment.
arXiv Detail & Related papers (2023-03-02T14:58:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.