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.

翻译：生成式人工智能（GenAI）使用量的急剧增长，凸显了对人工智能（AI）生成的多维数据进行负责任管理、使用和传播的安全高效机制的需求。本文提出了一种基于区块链的版权追溯框架，称为环形振荡器-奇异值分解（RO-SVD）。该框架引入分解计算来近似由硬件熵源生成的低秩矩阵，并在设备层面建立了一种AI生成内容（AIGC）的版权追溯机制。通过利用现场可编程门阵列（FPGA）的并行性和可重构性，我们的框架可以轻松构建在现有的AI加速设备上，并为AIGC新出现的版权问题提供一种低成本解决方案。基于对多种适用于AI的FPGA的全面分析和板载实验，我们开发了一个硬件/软件（HW/SW）协同设计原型。以AI生成图像作为案例研究，我们的框架证明了其有效性，并强调了其定制性、不可预测性、高效性、可管理性和可重构性。据我们所知，这是首个专门针对AI生成内容讨论并实现版权追溯的实用性硬件研究。