Don't FREAK Out: A Frequency-Inspired Approach to Detecting Backdoor Poisoned Samples in DNNs

Autor:	Hammoud, Hasan Abed Al Kader, Bibi, Adel, Torr, Philip H. S., Ghanem, Bernard
Rok vydání:	2023
Předmět:	Computer Science - Cryptography and Security Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted at CVPRW (The Art of Robustness)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.13211 Zobrazit plný text záznamu View this record from Arxiv