A Robust Adversarial Network-Based End-to-End Communications System With Strong Generalization Ability Against Adversarial Attacks
This work addresses security vulnerabilities in communications systems for applications like wireless networks, though it appears incremental as it builds on existing GAN frameworks.
The authors tackled the problem of defending against adversarial attacks in end-to-end communications systems by proposing a GAN-based defensive mechanism, resulting in a system that outperforms conventional and adversarial training methods while maintaining good performance under no attacks.
We propose a novel defensive mechanism based on a generative adversarial network (GAN) framework to defend against adversarial attacks in end-to-end communications systems. Specifically, we utilize a generative network to model a powerful adversary and enable the end-to-end communications system to combat the generative attack network via a minimax game. We show that the proposed system not only works well against white-box and black-box adversarial attacks but also possesses excellent generalization capabilities to maintain good performance under no attacks. We also show that our GAN-based end-to-end system outperforms the conventional communications system and the end-to-end communications system with/without adversarial training.