Abstract
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.
Abstract (translated)
在本文中，我们研究了 Deep Neural Networks (DNNs) 在面对干净样本和有毒样本时的频率响应。我们的分析表明，这两种样本之间存在着显著的差异。基于这些发现，我们提出了FREAK，一个基于频率的有毒样本检测算法，其简单而有效。我们的实验结果表明，FREAK不仅可以对抗频率恶意后门攻击，还可以对抗某些空间攻击。我们的工作只是利用这些 insights 的第一步。我们相信，我们的分析和提出的防御机制将为未来恶意后门防御的研究和发展提供基础。
URL
https://arxiv.org/abs/2303.13211
