Robust Adversarial Attacks Against DNN-Based Wireless Communication Systems
This addresses security risks in wireless communications for users and operators, but it is incremental as it builds on known adversarial attack methods.
The paper tackles the vulnerability of DNN-based wireless communication systems to adversarial attacks by proposing a robust, input-agnostic attack using Universal Adversarial Perturbations and a GAN for undetectability, showing it significantly outperforms existing attacks even with defenses in place.
Deep Neural Networks (DNNs) have become prevalent in wireless communication systems due to their promising performance. However, similar to other DNN-based applications, they are vulnerable to adversarial examples. In this work, we propose an input-agnostic, undetectable, and robust adversarial attack against DNN-based wireless communication systems in both white-box and black-box scenarios. We design tailored Universal Adversarial Perturbations (UAPs) to perform the attack. We also use a Generative Adversarial Network (GAN) to enforce an undetectability constraint for our attack. Furthermore, we investigate the robustness of our attack against countermeasures. We show that in the presence of defense mechanisms deployed by the communicating parties, our attack performs significantly better compared to existing attacks against DNN-based wireless systems. In particular, the results demonstrate that even when employing well-considered defenses, DNN-based wireless communications are vulnerable to adversarial attacks.