A Unified Framework for Backdoor Trigger Segmentation.

Abstract

Recently, backdoor attacks on Deep Neural Networks (DNNs) have raised urgent security threats, which can manipulate the behavior of an attacked model by embedding the backdoor trigger into the input. Since triggers can be designed to be stealthy and hard to recognize by the naked eye, segmenting these triggers in backdoor samples becomes a significant challenge. However, finding triggers embedded by the attacker can be crucial for analyzing the attacks and formulating a defense strategy. Therefore, in this paper, we propose the Backdoor Trigger Segmentation (BTS) task with a comprehensive benchmark consisting of 8 attack methods, 8 unique triggers, and 179 attack settings for image or text data. Moreover, we construct a mathematical system for BTS, abstracting various backdoor triggers into a unified theoretical framework. Based on the theoretical guarantees, we propose a unified Trigger Locator (TriLoc) algorithm to segment various triggers in backdoor samples of both image and text modalities, without prior knowledge of triggers. Extensive experimental results on our benchmark demonstrate the superior performance of our algorithm compared to state-of-the-art methods. Our benchmark and code are available at https://github.com/LivXue/Backdoor-Trigger-Segmentation.