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Article overview
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CASSOCK: Viable Backdoor Attacks against DNN in The Wall of Source-Specific Backdoor Defences | | Shang Wang
; Yansong Gao
; Anmin Fu
; Zhi Zhang
; Yuqing Zhang
; Willy Susilo
; | | Date: |
1 Jun 2022 | | Abstract: | Backdoor attacks have been a critical threat to deep neural network (DNN).
However, most existing countermeasures focus on source-agnostic backdoor
attacks (SABAs) and fail to defeat source-specific backdoor attacks (SSBAs).
Compared to an SABA, an SSBA activates a backdoor when an input from
attacker-chosen class(es) is stamped with an attacker-specified trigger, making
itself stealthier and thus evade most existing backdoor mitigation.
Nonetheless, existing SSBAs have trade-offs on attack success rate (ASR, a
backdoor is activated by a trigger input from a source class as expected) and
false positive rate (FPR, a backdoor is activated unexpectedly by a trigger
input from a non-source class). Significantly, they can still be effectively
detected by the state-of-the-art (SOTA) countermeasures targeting SSBAs. This
work overcomes efficiency and effectiveness deficiencies of existing SSBAs,
thus bypassing the SOTA defences. The key insight is to construct desired
poisoned and cover data during backdoor training by characterising SSBAs
in-depth. Both data are samples with triggers: the cover/poisoned data from
non-source/source class(es) holds ground-truth/target labels. Therefore, two
cover/poisoned data enhancements are developed from trigger style and content,
respectively, coined CASSOCK. First, we leverage trigger patterns with
discrepant transparency to craft cover/poisoned data, enforcing triggers with
heterogeneous sensitivity on different classes. The second enhancement chooses
the target class features as triggers to craft these samples, entangling
trigger features with the target class heavily. Compared with existing SSBAs,
CASSOCK-based attacks have higher ASR and low FPR on four popular tasks: MNIST,
CIFAR10, GTSRB, and LFW. More importantly, CASSOCK has effectively evaded three
defences (SCAn, Februus and extended Neural Cleanse) already defeat existing
SSBAs effectively. | | Source: | arXiv, 2206.00145 | | Services: | Forum | Review | PDF | Favorites | |
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