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. Our proposed CDI-aware GAN can be used as an attacker and a defender. In attack scenarios, the CDI-aware GAN demonstrates its prowess by generating robust adversarial perturbations that effectively deceive the target classifier, outperforming known methods. Furthermore, CDI-aware GAN as a defender significantly improves the target classifier's resilience against adversarial attacks.

翻译：我们提出了一种信道分布信息（CDI）感知的生成对抗网络（GAN），旨在解决无线通信系统中对抗攻击特有的挑战。该CDI感知GAN中的生成器将随机输入噪声映射至特征空间，生成旨在欺骗目标调制分类器的扰动。其判别器扮演双重角色：一个判别器强制扰动服从高斯分布，使其与高斯噪声无法区分；另一个则确保这些扰动能考虑真实信道效应并近似无信道扰动。我们提出的CDI感知GAN既可作为攻击方也可作为防御方。在攻击场景下，该CDI感知GAN通过生成能有效欺骗目标分类器的鲁棒对抗扰动，性能优于现有方法。此外，作为防御方时，CDI感知GAN显著提升了目标分类器抵御对抗攻击的韧性。