The dramatic surge in the utilisation of generative artificial intelligence
(GenAI) underscores the need for a secure and efficient mechanism to
responsibly manage, use and disseminate multi-dimensional data generated by
artificial intelligence (AI). In this paper, we propose a blockchain-based
copyright traceability framework called ring oscillator-singular value
decomposition (RO-SVD), which introduces decomposition computing to approximate
low-rank matrices generated from hardware entropy sources and establishes an
AI-generated content (AIGC) copyright traceability mechanism at the device
level. By leveraging the parallelism and reconfigurability of
field-programmable gate arrays (FPGAs), our framework can be easily constructed
on existing AI-accelerated devices and provide a low-cost solution to emerging
copyright issues of AIGC. We developed a hardware-software (HW/SW) co-design
prototype based on comprehensive analysis and on-board experiments with
multiple AI-applicable FPGAs. Using AI-generated images as a case study, our
framework demonstrated effectiveness and emphasised customisation,
unpredictability, efficiency, management and reconfigurability. To the best of
our knowledge, this is the first practical hardware study discussing and
implementing copyright traceability specifically for AI-generated content.

提出了一种基于区块链的版权可追溯性框架 RO-SVD，利用分解计算来近似从硬件熵源产生的低秩矩阵，建立了一种设备级的 AI 生成内容版权可追溯机制，通过现有的 AI 加速设备构建，并提供了针对新兴版权问题的低成本解决方案。