CASSOCK: Viable Backdoor Attacks against DNN in the Wall of Source-Specific Backdoor Defenses
Shang Wang, Yansong Gao, Anmin Fu, Zhi Zhang, Yuqing Zhang, Willy Susilo, Dongxi Liu · 2023
As a critical threat to deep neural networks (DNNs), backdoor attacks can be categorized into two types, i.e., source-agnostic backdoor attacks (SABAs) and source-specific backdoor attacks (SSBAs). Compared to traditional SABAs, SSBAs are more advanced in that they have superior stealthier in bypassing mainstream countermeasures that are effective against SABAs. Nonetheless, existing SSBAs suffer from two major limitations. First, they can hardly achieve a good trade-off between ASR (attack success rate) and FPR (false positive rate). Besides, they can be effectively detected by the state-of-the-art (SOTA) countermeasures (e.g., SCAn [40]).