Cryptoanalysis of the modified diffractive-imaging-based image encryption by deep learning attack

The conventional diffractive-imaging-based image encryption (CDIE) proposed in 2010 has drawn much attention in the last decade. A new modified diffractive-imaging-based image encryption (MDIE) with good non-linearity was proposed in 2019. Inspired by the cryptoanalysis method based on the convolutional neural networks, we propose a model trained with large numbers of ciphertext-plaintext pairs to crack the new cryptosystem. The proposed model has two blocks, the first block is employed for extracting the features of ciphertext and the second one is used for recovering the plaintext according to these extracted features. Compared with existing cryptoanalysis methods based on the convolutional neural networks, the proposed model has better generalization. We hope this structure can help researchers to solve other optical cryptoanalysis problems. To our knowledge, this is the first time to make the CNN-based attack can be used beyond the MNIST dataset, including both the handwriting dataset and fashion dataset. This work proves the CNN-based attack can be used in general situations. The analysis of validity and robustness is presented. Besides, the experimental results are conducted to validate the proposed method.