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This paper presents a deep learning-based framework for executing undetectable adversarial attacks on wireless autoencoders, addressing the challenges posed by cumulative leakage interference (CLI) from multiple adversaries in dynamic environments. By implementing a transmit power control mechanism and a conditional generative adversarial attack, the framework effectively reduces interference leakage and generates adaptive perturbations that closely mimic legitimate signals. Simulation results indicate that this approach significantly enhances attack undetectability, aggressivity, and adaptability compared to existing benchmarks.
Undetectable adversarial attacks on wireless autoencoders can be achieved by intelligently managing transmit power and generating adaptive perturbations that mimic legitimate signals.
Adversarial attacks can degrade the legitimate decision performance in wireless autoencoder communications. However, in complex scenarios with multiple adversaries, the cumulative leakage interference (CLI) caused by the multiple parallel attacks increases the chance of detecting the attacks, while dynamical environments also make the fixed attack strategies difficult to have stable effectiveness. To jointly enhance the undetectability, aggressivity and adaptability of adversarial attacks, we propose a deep learning based intelligent attack framework. Specifically, considering the CLI caused by the multiple parallel attacks, a deep neural network based transmit power control is established to reduce the interference leakage by regulating the transmit power of these adversaries, thereby improving the undetectability. Furthermore, to enhance the attack effectiveness and stability in the dynamic environment, the conditional generative adversarial attack is further developed. The generator takes the attack channel information as the conditional input to produce the perturbating signals to mislead the discriminator by making the attacked received signals resemble the clean received signals, while the discriminator distinguishes between the two under the same condition. Through the adversarial training, the generator can learn to create adaptive perturbating signals with enhanced attack performance. Simulation results demonstrate that the proposed framework outperforms benchmarks in terms of attack undetectability, aggressivity and adaptability.
