Abstract Physical unclonable functions (PUFs) are complex physical objects that aim at overcoming the vulnerabilities of traditional cryptographic keys, promising a robust class of security primitives for different applications. Optical PUFs present advantages over traditional electronic realizations, namely, a stronger unclonability, but suffer from problems of reliability and weak unpredictability of the key. We here develop a two-step PUF generation strategy based on deep learning, which associates reliable keys verified against the National Institute of Standards and Technology (NIST) certification standards of true random generators for cryptography. The idea explored in this work is to decouple the design of the PUFs from the key generation and train a neural architecture to learn the mapping algorithm between the key and the PUF. We report experimental results with all-optical PUFs realized in silica aerogels and analyzed a population of 100 generated keys, each of 10,000 bit length. The key generated passed all tests required by the NIST standard, with proportion outcomes well beyond the NIST’s recommended threshold. The two-step key generation strategy studied in this work can be generalized to any PUF based on either optical or electronic implementations. It can help the design of robust PUFs for both secure authentications and encrypted communications.

中文翻译：

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通过对二氧化硅气凝胶中物理不可克隆功能的深度学习，生成 NIST 认证的安全密钥

摘要 物理不可克隆函数 (PUF) 是复杂的物理对象，旨在克服传统加密密钥的漏洞，为不同的应用程序提供了一类强大的安全原语。光学 PUF 与传统电子实现相比具有优势，即更强的不可克隆性，但存在可靠性和密钥不可预测性弱的问题。我们在这里开发了一种基于深度学习的两步 PUF 生成策略，该策略将根据美国国家标准与技术研究院 (NIST) 密码学真随机生成器认证标准验证的可靠密钥相关联。在这项工作中探索的想法是将 PUF 的设计与密钥生成分离，并训练神经架构来学习密钥和 PUF 之间的映射算法。我们报告了在二氧化硅气凝胶中实现的全光 PUF 的实验结果，并分析了 100 个生成的密钥，每个密钥的长度为 10,000 位。生成的密钥通过了 NIST 标准要求的所有测试，结果比例远远超出了 NIST 的推荐阈值。在这项工作中研究的两步密钥生成策略可以推广到任何基于光学或电子实现的 PUF。它可以帮助设计用于安全身份验证和加密通信的稳健 PUF。在这项工作中研究的两步密钥生成策略可以推广到任何基于光学或电子实现的 PUF。它可以帮助设计用于安全身份验证和加密通信的稳健 PUF。在这项工作中研究的两步密钥生成策略可以推广到任何基于光学或电子实现的 PUF。它可以帮助设计用于安全身份验证和加密通信的稳健 PUF。