In this paper we investigate the frequency sensitivity of Deep Neural Networks (DNNs) when presented with clean samples versus poisoned samples. Our analysis shows significant disparities in frequency sensitivity between these two types of samples. Building on these findings, we propose FREAK, a frequency-based poisoned sample detection algorithm that is simple yet effective. Our experimental results demonstrate the efficacy of FREAK not only against frequency backdoor attacks but also against some spatial attacks. Our work is just the first step in leveraging these insights. We believe that our analysis and proposed defense mechanism will provide a foundation for future research and development of backdoor defenses.

翻译：本文研究了深度神经网络（DNN）在处理干净样本与污染样本时的频率敏感性。分析表明，这两类样本在频率敏感性上存在显著差异。基于这些发现，我们提出了FREAK——一种简单而有效的基于频率的污染样本检测算法。实验结果表明，FREAK不仅对频率后门攻击有效，还能抵御部分空间攻击。我们的工作仅是初步探索这些见解。我们相信，本研究的分析及所提出的防御机制将为后门防御的未来研究与发展奠定基础。