  This paper proposes a new approach to detecting neural Trojans on Deep Neural
Networks during inference. This approach is based on monitoring the inference
of a machine learning model, computing the attribution of the model's decision
on different features of the input, and then statistically analyzing these
attributions to detect whether an input sample contains the Trojan trigger. The
anomalous attributions, aka misattributions, are then accompanied by
reverse-engineering of the trigger to evaluate whether the input sample is
truly poisoned with a Trojan trigger. We evaluate our approach on several
benchmarks, including models trained on MNIST, Fashion MNIST, and German
Traffic Sign Recognition Benchmark, and demonstrate the state of the art
detection accuracy.
