Abstract The only known approach that can break the diffractive-imaging-based encryption (DIBE) was proposed by Li and Shi in 2015. However, their approach works under the assumption that the phase distribution of the random phase masks (RPMs) is within [0, π]. In other words, it is no longer effective when such requirement is not fulfilled. In this paper, we propose a universal method, referred to as learning-based chosen-plaintext attack (L-CPA), to break DIBE. The L-CPA enables one to recover the plaintext from the ciphertext by aid of a well-trained artificial neural network (ANN), regardless of the phase distribution of the RPMs. Furthermore, the proposal can be accomplished with no need of knowing the details of the optical arrangement of DIBE. To our best knowledge, this is the first paper that reveals the absolute insecurity of DIBE against CPA. Numerical simulations are presented to demonstrate the effectiveness and feasibility of the proposal.

中文翻译：

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基于学习的基于衍射成像的加密方案的选择明文攻击

摘要 唯一已知的可以破解基于衍射成像的加密 (DIBE) 的方法是由 Li 和 Shi 在 2015 年提出的。然而，他们的方法在假设随机相位掩模 (RPM) 的相位分布在 [ 0, π]。换句话说，当不满足该要求时，它就不再有效。在本文中，我们提出了一种通用方法，称为基于学习的选择明文攻击（L-CPA），以破解 DIBE。无论 RPM 的相位分布如何，L-CPA 都可以借助训练有素的人工神经网络 (ANN) 从密文中恢复明文。此外，无需了解 DIBE 光学布置的细节即可完成该提议。据我们所知，这是第一篇揭示 DIBE 对 CPA 绝对不安全的论文。数值模拟被提出来证明该建议的有效性和可行性。