  Deep Learning backdoor attacks have a threat model similar to traditional
cyber attacks. Attack forensics, a critical counter-measure for traditional
cyber attacks, is hence of importance for defending model backdoor attacks. In
this paper, we propose a novel model backdoor forensics technique. Given a few
attack samples such as inputs with backdoor triggers, which may represent
different types of backdoors, our technique automatically decomposes them to
clean inputs and the corresponding triggers. It then clusters the triggers
based on their properties to allow automatic attack categorization and
summarization. Backdoor scanners can then be automatically synthesized to find
other instances of the same type of backdoor in other models. Our evaluation on
2,532 pre-trained models, 10 popular attacks, and comparison with 9 baselines
show that our technique is highly effective. The decomposed clean inputs and
triggers closely resemble the ground truth. The synthesized scanners
substantially outperform the vanilla versions of existing scanners that can
hardly generalize to different kinds of attacks.
