Don’t FREAK out: a frequency-inspired approach to detecting backdoor poisoned samples in DNNs
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 fre...
| Main Authors: | , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
IEEE
2023
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