A learning-based method of attack on optical asymmetric cryptosystems

摘要

The so-called Optical Asymmetric Cryptosystems (OACs) have attracted more and more attention due to the unique mechanism of encryption/decryption and higher security level resulted from the involved nonlinear operations. Meanwhile, several attack methods have also been reported to analyze the security risk of typical OACs. In this work, we first stated our basic views about two famous OACs and then demonstrated that they are both vulnerable to a deep-learning-based strategy. Thanks to a carefully designed and trained (by known plaintext-ciphertext pairs) generative adversarial network (GAN), an attacker could intercept enough high-frequency components of subsequent plaintext leading to successful retrieval. Compared with the previous amplitude-phase-retrieval-based methods, the proposed learning-based scheme has a major advantage of retrieving plaintexts with high quality in real-time. Numerical simulations demonstrate the feasibility and effectiveness of the proposed learning-based attack method.