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Deep Learning backdoor attacks have a threat model similar to traditionalcyber attacks. Attack forensics, a critical counter-measure for traditionalcyber attacks, is hence of importance for defending model backdoor attacks. Inthis paper, we propose a novel model backdoor forensics technique. Given a fewattack samples such as inputs with backdoor triggers, which may representdifferent types of backdoors, our technique automatically decomposes them toclean inputs and the corresponding triggers. It then clusters the triggersbased on their properties to allow automatic attack categorization andsummarization. Backdoor scanners can then be automatically synthesized to findother instances of the same type of backdoor in other models. Our evaluation on2,532 pre-trained models, 10 popular attacks, and comparison with 9 baselinesshow that our technique is highly effective. The decomposed clean inputs andtriggers closely resemble the ground truth. The synthesized scannerssubstantially outperform the vanilla versions of existing scanners that canhardly generalize to different kinds of attacks.

BEAGLE: Forensics of Deep Learning Backdoor Attack for Better Defense