Novel fully convolutional network for cryptanalysis of cryptosystem by equal modulus decomposition

Abstract

The optical cryptosystem based on equal modulus decomposition (EMD) has attracted wide attention due to its remarkable anti-attack characteristics. In this paper, we propose a novel fully convolutional network model, which is an end-to-end deep learning method, to attack the EMD-based cryptosystem. The trained network model can retrieve plaintext after inputting many ciphertext-plaintext pairs and optimizing parameters. Numerical simulation results and analysis show that EMD-based cryptosystems by Fourier and Fresnel transforms are both vulnerable to our proposed method. Furthermore, the proposed network model can also successfully attack the interference-based cryptosystem. Compared with other methods, the proposed attack method has the advantages of shorter training time and stronger generalization ability. The proposed method provides a new approach for cryptoanalysis of cryptosystem based on EMD.