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arXiv · 2111.14309

A General Framework for Defending Against Backdoor Attacks via Influence Graph

Xiaofei Sun·Jiwei Li·Xiaoya Li·Ziyao Wang·Tianwei Zhang·Han Qiu·Fei Wu·Chun Fan
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Abstract

In this work, we propose a new and general framework to defend against backdoor attacks, inspired by the fact that attack triggers usually follow a \textsc{specific} type of attacking pattern, and therefore, poisoned training examples have greater impacts on each other during training. We introduce the notion of the {\it influence graph}, which consists of nodes and edges respectively representative of individual training points and associated pair-wise influences. The influence between a pair of training points represents the impact of removing one training point on the prediction of another, approximated by the influence function \citep{koh2017understanding}. Malicious training points are extracted by finding the maximum average sub-graph subject to a particular size. Extensive experiments on computer vision and natural language processing tasks demonstrate the effectiveness and generality of the proposed framework.

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Xiaofei Sun, Jiwei Li, Xiaoya Li, Ziyao Wang, Tianwei Zhang, Han Qiu, Fei Wu, Chun Fan. 2021-11-29. A General Framework for Defending Against Backdoor Attacks via Influence Graph. https://arxiv.org/abs/2111.14309

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