Visually Secure Deep Joint Source-Channel Coding With Chaotic Map Against Deep Known-Plaintext Attack

Abstract

In recent years, deep learning-based joint source-channel coding (DJSCC) has gained significant attention for its impressive performance in image transmission. Unlike traditional separate source-channel coding (SSCC) methods, DJSCC performs particularly well in low signal-to-noise ratio (SNR) and limited bandwidth environments. However, ensuring the security of private information during transmission remains a critical concern. A notable limitation of DJSCC is its incompatibility with traditional encryption methods used for secure communications, making it vulnerable to eavesdropping attacks. To address this issue, we propose integrating a chaotic map encryption method into the DJSCC framework for secure wireless image transmission. This approach leverages chaotic sequence to shuffle the position of the elements in latent space without altering the values of the latent tensor. This allows the encryption process to be designed independently of DJSCC, eliminating the need for re-training the end-to-end model. Our proposed method preserves DJSCC’s superior transmission characteristics, ensuring high-quality image reconstruction at the receiver, while effectively ensuring the security against deep learning-based known plaintext attacks (Deep KPA).