AT-GAN: A Backdoor Attack Against Radio Signals Modulation Based on Adaptive Trigger

Recently, backdoor attacks posed a new security threat against radio signals modulation models. The attacked model performs well on benign samples, whereas performs abnormally on backdoor samples. Current backdoor attacks usually inject static trigger into benign samples, which can be easily detected with static trigger patterns and large perturbation. In this paper, inspired by the Generative Adversarial Network (GAN), we proposed a backdoor attack against radio signals modulation based on adaptive trigger (AT-GAN) to learn the patterns of adaptive trigger, which can generate different trigger for specific radio signal. AT-GAN is composed of Adaptive Backdoor Generate Network (A-BaN), discriminator and radio signals modulation model. A-BaN is trained to generate adaptive triggers, discriminator is used to limit the perturbation of triggers and modulation model is poisoned based on generated poisoned samples. By a three-player game, A-BaN can generate adaptive triggers with small perturbation. The experimental results on two benchmark datasets demonstrate that our method can achieve better attack performance and reduce the detection rate of backdoor attacks compared to baseline backdoor attacks.