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.

中文翻译：

---

通过等模分解对密码系统进行密码分析的新型全卷积网络

基于等模分解（EMD）的光学密码系统由于其出色的抗攻击特性而备受关注。在本文中，我们提出了一种新颖的全卷积网络模型，这是一种端到端的深度学习方法，可以攻击基于EMD的密码系统。经过训练的网络模型可以在输入许多密文-明文对并优化参数后检索纯文本。数值仿真结果和分析表明，基于傅里叶和菲涅尔变换的基于EMD的密码系统都容易受到我们提出的方法的攻击。此外，所提出的网络模型还可以成功地攻击基于干扰的密码系统。与其他方法相比，该攻击方法具有训练时间短，泛化能力强的优点。