Lotus: Evasive and Resilient Backdoor Attacks through Sub-Partitioning

Abstract

Backdoor attack poses a significant security threat to Deep Learning applications. Existing attacks are often not evasive to established backdoor detection techniques. This susceptibility primarily stems from the fact that these attacks typically leverage a universal trigger pattern or transfor-mation function, such that the trigger can cause misclas-sification for any input. In response to this, recent papers have introduced attacks using sample-specific invisible trig-gers crafted through special transformation functions. While these approaches manage to evade detection to some extent, they reveal vulnerability to existing backdoor mitigation techniques. To address and enhance both evasiveness and resilience, we introduce a novel backdoor attack Lotus. Specifically, it leverages a secret function to separate sam-ples in the victim class into a set of partitions and applies unique triggers to different partitions. Furthermore, Lotus incorporates an effective trigger focusing mechanism, en-suring only the trigger corresponding to the partition can induce the backdoor behavior. Extensive experimental re-sults show that Lotus can achieve high attack success rate across 4 datasets and 7 model structures, and effectively evading 13 backdoor detection and mitigation techniques. The code is available at https://github.com/Megum1/LOTUS.