  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.

Cryptography and Security