Cryptanalysis on double random phase encoding with deep learning
Wenqi He, Han Hai, Meihua Liao, Dajiang Lu, Xiang Peng · 2018
Random-phase-based optical image encryption techniques have drawn a lot of attention in recent years. However, in this contribution those schemes have been demonstrated to be vulnerable to chosen-plaintext attack (CPA) by employing the deep learning strategy. Specifically, by optimizing the parameters, the chosen deep neural network (DNN) can be trained to learn the sensing of an optical cryptosystem and thus get the ability to reconstruct any plaintext image from its corresponding ciphertext. A set of numerical simulation results have been further provided to shown its ability on cracking not only the classical double random phase encryption (DRPE), but also the tripe random-phase encryption (TRPE).