A Photonic Physically Unclonable Function’s Resilience to Multiple-Valued Machine Learning Attacks

Authors: Jessie M. Henderson, Elena R. Henderson, Clayton A. Harper, Hiva Shahoei, William V. Oxford, Eric C. Larson, Duncan L. MacFarlane, Mitchell A. Thornton
Published: 2024-03-02

Source: https://arxiv.org/abs/2403.01299

PDF: https://arxiv.org/pdf/2403.01299

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モデル性能評価 機械学習手法 ウォーターマーキング

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Abstract

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.