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.
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