论文标题
自动调制分类的频谱集中频率对抗攻击
Spectrum Focused Frequency Adversarial Attacks for Automatic Modulation Classification
论文作者
论文摘要
人工智能（AI）技术为自动调制识别（AMC）提供了潜在的解决方案。不幸的是，基于AI的AMC模型容易受到对抗性示例的影响，这严重威胁了AI在AMC中的高效，安全和可信赖的应用。这个问题吸引了研究人员的注意。关于对抗性攻击和防御的各种研究在螺旋中演变。但是，现有的对抗攻击方法都是在时域设计的。由于时间域中的突然更新，他们在频域中引入了更多高频组件。对于此问题，从频域的角度来看，我们提出了针对AMC模型的频谱频率对抗攻击（SFFAA），并进一步借鉴了元学习的概念，建议一种元SFFAA算法，以提高黑盒攻击中的可传递性。广泛的实验，定性和定量指标表明，所提出的算法可以将对抗能集中在信号所在的频谱上，从而显着改善了对抗性攻击性能，同时保持频域中的隐藏性。
Artificial intelligence (AI) technology has provided a potential solution for automatic modulation recognition (AMC). Unfortunately, AI-based AMC models are vulnerable to adversarial examples, which seriously threatens the efficient, secure and trusted application of AI in AMC. This issue has attracted the attention of researchers. Various studies on adversarial attacks and defenses evolve in a spiral. However, the existing adversarial attack methods are all designed in the time domain. They introduce more high-frequency components in the frequency domain, due to abrupt updates in the time domain. For this issue, from the perspective of frequency domain, we propose a spectrum focused frequency adversarial attacks (SFFAA) for AMC model, and further draw on the idea of meta-learning, propose a Meta-SFFAA algorithm to improve the transferability in the black-box attacks. Extensive experiments, qualitative and quantitative metrics demonstrate that the proposed algorithm can concentrate the adversarial energy on the spectrum where the signal is located, significantly improve the adversarial attack performance while maintaining the concealment in the frequency domain.
