  The backdoor attack, where the adversary uses inputs stamped with triggers
(e.g., a patch) to activate pre-planted malicious behaviors, is a severe threat
to Deep Neural Network (DNN) models. Trigger inversion is an effective way of
identifying backdoor models and understanding embedded adversarial behaviors. A
challenge of trigger inversion is that there are many ways of constructing the
trigger. Existing methods cannot generalize to various types of triggers by
making certain assumptions or attack-specific constraints. The fundamental
reason is that existing work does not consider the trigger's design space in
their formulation of the inversion problem. This work formally defines and
analyzes the triggers injected in different spaces and the inversion problem.
Then, it proposes a unified framework to invert backdoor triggers based on the
formalization of triggers and the identified inner behaviors of backdoor models
from our analysis. Our prototype UNICORN is general and effective in inverting
backdoor triggers in DNNs. The code can be found at
https://github.com/RU-System-Software-and-Security/UNICORN.
