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.

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