Physically unclonable functions (PUFs) identify integrated circuits using nonlinearly-related challenge-response pairs (CRPs). Ideally, the relationship between challenges and corresponding responses is unpredictable, even if a subset of CRPs is known. Previous work developed a photonic PUF offering improved security compared to non-optical counterparts. Here, we investigate this PUF's susceptibility to Multiple-Valued-Logic-based machine learning attacks. We find that approximately 1,000 CRPs are necessary to train models that predict response bits better than random chance. Given the significant challenge of acquiring a vast number of CRPs from a photonic PUF, our results demonstrate photonic PUF resilience against such attacks.

翻译：物理不可克隆函数（PUF）利用非线性相关的挑战-响应对（CRP）来标识集成电路。理想情况下，即便已知部分CRP子集，挑战与对应响应之间的关系仍不可预测。此前研究提出了一种光子PUF，与光学类器件相比具有更高的安全性。本文研究了此类PUF对基于多值逻辑的机器学习攻击的敏感性。研究发现，要训练出能以优于随机猜测的概率预测响应比特的模型，大约需要1000个CRP。考虑到从光子PUF中获取大量CRP存在显著困难，我们的结果表明光子PUF对此类攻击具有鲁棒性。