Novel fully convolutional network for cryptanalysis of cryptosystem by equal modulus decomposition
Fan Wang, Renjie Ni, Jun Wang, Zheng Dong Zhu, Xudong Chen, Yu Hen Hu · Laser Physics Letters · 2020
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.