  Despite their success and popularity, deep neural networks (DNNs) are
vulnerable when facing backdoor attacks. This impedes their wider adoption,
especially in mission critical applications. This paper tackles the problem of
Trojan detection, namely, identifying Trojaned models -- models trained with
poisoned data. One popular approach is reverse engineering, i.e., recovering
the triggers on a clean image by manipulating the model's prediction. One major
challenge of reverse engineering approach is the enormous search space of
triggers. To this end, we propose innovative priors such as diversity and
topological simplicity to not only increase the chances of finding the
appropriate triggers but also improve the quality of the found triggers.
Moreover, by encouraging a diverse set of trigger candidates, our method can
perform effectively in cases with unknown target labels. We demonstrate that
these priors can significantly improve the quality of the recovered triggers,
resulting in substantially improved Trojan detection accuracy as validated on
both synthetic and publicly available TrojAI benchmarks.

Computer Vision and Pattern Recognition;Computational Geometry