智能干扰：对无线自动编码器的不可检测攻击
Intelligent Disruption: Undetectable Attacks on Wireless Autoencoders
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中文总结 AI 辅助
研究针对无线自动编码器通信的对抗攻击问题，提出基于深度学习的智能攻击框架，通过控制发射功率减少干扰泄漏提升不可检测性，发展条件生成对抗攻击增强动态环境下的攻击效果与稳定性，仿真显示该框架性能优于基准。
中文摘要 AI 辅助
对抗攻击会降低无线自动编码器通信中的合法决策性能。在多对手复杂场景中，多个并行攻击造成的累积泄漏干扰增加了攻击被检测到的机会，动态环境也使固定攻击策略难以保持稳定有效性。为联合增强对抗攻击的不可检测性、攻击性和适应性，提出基于深度学习的智能攻击框架。考虑并行攻击的CLI，建立基于深度神经网络的发射功率控制以减少干扰泄漏，提高不可检测性。还进一步发展条件生成对抗攻击，通过对抗训练使生成器学习创建具有增强攻击性能的自适应干扰信号。仿真结果表明该框架在攻击不可检测性、攻击性和适应性方面优于基准。
英文摘要
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.
