Applying Deep Learning to Known-Plaintext Attack on Chaotic Image Encryption Schemes

Fusen Wang; Jun Sang; Chunlin Huang; Bin Cai; Hong Xiang; Nong Sang

ICASSP 2022accepted0 citations

Applying Deep Learning to Known-Plaintext Attack on Chaotic Image Encryption Schemes

Fusen Wang, Jun Sang, Chunlin Huang, Bin Cai, Hong Xiang, Nong Sang

Abstract

In this paper, we demonstrate that traditional chaotic encryption schemes are vulnerable to the known-plaintext attack (KPA) with deep learning. Considering the decryption process as image restoration based on deep learning, we apply Convolutional Neural Network to perform known-plaintext attack on chaotic cryptosystems. We design a network to learn the operation mechanism of chaotic cryptosystems, and utilize the trained network as the decryption system. To prove the effectiveness, we select three existing chaotic encryption schemes as the attacked targets. The experimental results demonstrate that deep learning can be applied to known-plaintext attack against chaotic cryptosystems successfully. Compared with traditional attack methods for chaotic cryptosystems, the proposed method shows obvious advantages: (1) One neural network may be applied to cryptanalysis of various chaotic cryptosystems, not limited to specific one; (2) the proposed method is significantly convenient and cost-efficient. This paper provides a new idea for the cryptanalysis of chaotic cryptosystems.

BibTeX

@inproceedings{icassp2022_applyingdeeplear,
  title = {Applying Deep Learning to Known-Plaintext Attack on Chaotic Image Encryption Schemes},
  author = {Fusen Wang and Jun Sang and Chunlin Huang and Bin Cai and Hong Xiang and Nong Sang},
  booktitle = {ICASSP 2022},
  year = {2022}
}