  An emerging amount of intelligent applications have been developed with the
surge of Machine Learning (ML). Deep Neural Networks (DNNs) have demonstrated
unprecedented performance across various fields such as medical diagnosis and
autonomous driving. While DNNs are widely employed in security-sensitive
fields, they are identified to be vulnerable to Neural Trojan (NT) attacks that
are controlled and activated by the stealthy trigger. We call this vulnerable
model adversarial artificial intelligence (AI). In this paper, we target to
design a robust Trojan detection scheme that inspects whether a pre-trained AI
model has been Trojaned before its deployment. Prior works are oblivious of the
intrinsic property of trigger distribution and try to reconstruct the trigger
pattern using simple heuristics, i.e., stimulating the given model to incorrect
outputs. As a result, their detection time and effectiveness are limited. We
leverage the observation that the pixel trigger typically features spatial
dependency and propose TAD, the first trigger approximation based Trojan
detection framework that enables fast and scalable search of the trigger in the
input space. Furthermore, TAD can also detect Trojans embedded in the feature
space where certain filter transformations are used to activate the Trojan. We
perform extensive experiments to investigate the performance of the TAD across
various datasets and ML models. Empirical results show that TAD achieves a
ROC-AUC score of 0:91 on the public TrojAI dataset 1 and the average detection
time per model is 7:1 minutes.
