Adversarial Attacks and Defenses for Wireless Signal Classifiers Using CDI-aware GANs

We introduce a Channel Distribution Information (CDI)-aware Generative Adversarial Network (GAN), designed to address the unique challenges of adversarial attacks in wireless communication systems. The generator in this CDI-aware GAN maps random input noise to the feature space, generating perturbations intended to deceive a target modulation classifier. Its discriminators play a dual role: one enforces that the perturbations follow a Gaussian distribution, making them indistinguishable from Gaussian noise, while the other ensures these perturbations account for realistic channel effects and resemble no-channel perturbations. This approach crafts a new perturbation for each channel use, making it harder to detect using pilot signals. Our proposed CDI-aware GAN can be used as an attacker and a defender. As a defender, it significantly increases the resilience of the legitimate modulation classifier against adversarial attacks, outperforming known methods. Furthermore, as an attacker, the CDI-aware GAN effectively deceives the target classifier while transmitting a new perturbation signal for each channel use.