Transferable Unique Copyright Across AI Model Trading: A Blockchain-Driven Non-Fungible Token Approach

【Author】 Fan, Yixin; Hao, Guozhi; Wu, Jun

【Source】2022 IEEE 22ND INTERNATIONAL CONFERENCE ON SOFTWARE QUALITY, RELIABILITY, AND SECURITY COMPANION, QRS-C

【影响因子】

【Abstract】Currently, Machine Learning as a Service (MLaaS) greatly benefits artificial intelligence (AI) model trading. However, threats such as model piracy and patent grabbing devastatingly violate the copyright of AI models. Current invasive copyright protection solutions mainly rely on watermarking to embed specific information into AI models, which inevitably decreases the accuracy. While non-invasive schemes, such as adversarial samples, cannot guarantee uniqueness as the adversarial sample generation algorithm would be known to all traders, and thus need to be changed after trading. To enable the ownership information transferable across AI model trading, we propose a blockchain-driven Non-Fungible Token (NFT) approach for trading-oriented AI model copyright protection. We design a mapping mechanism from AI models parameters to NFTs which can identify uniqueness and ownership of AI models across trading. Besides, a reputation-based rewards and penalties scheme is proposed to prevent NFT piracy. Lastly, the evaluation verifies the applicability of our approach.

【Keywords】Blockchain; NFT; AI model trading; copyright protection

【发表时间】2022

【收录时间】2023-06-01

【文献类型】理论模型

【主题类别】

区块链应用-虚拟经济-NFT