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.

Multi-Image Encryption Based on Compressed Sensing and Deep Learning in Optical Gyrator Domain

“…Therefore, the proposed encryption scheme can resist CPA. Recently, some researchers have proposed CPA methods based on deep learning [39][40][41]. By inputting plaintext and corresponding ciphertext into the neural network, the encryption system can be broken.…”

Neural Networks-Based Cryptography: A Survey

“…Roughly speaking, cryptanalysis aims to test and analyze the security of cryptographic protocols by feeding different inputs to the cryptographic algorithm and analyzing the outputs in order to find a common or repetitive pattern in the outputs that might help find the secret key or even decrypt the ciphertext without access to the key. Machine learning can help learn from the data generated by the cryptographic algorithm and detect significant patterns [6]- [8] In late 90's and early 2000's, several cryptographic protocols using machine learning and deep learning models were proposed such as [9]- [11], but were deemed insecure and even some concrete attacks [12] were shown subsequently. The interest in neural network based cryptography took a dip because of the fact that simple computations, even as basic as exclusive-or (XOR) operation could not be computed by simple neural networks.…”