  A trojan backdoor is a hidden pattern typically implanted in a deep neural
network. It could be activated and thus forces that infected model behaving
abnormally only when an input data sample with a particular trigger present is
fed to that model. As such, given a deep neural network model and clean input
samples, it is very challenging to inspect and determine the existence of a
trojan backdoor. Recently, researchers design and develop several pioneering
solutions to address this acute problem. They demonstrate the proposed
techniques have a great potential in trojan detection. However, we show that
none of these existing techniques completely address the problem. On the one
hand, they mostly work under an unrealistic assumption (e.g. assuming
availability of the contaminated training database). On the other hand, the
proposed techniques cannot accurately detect the existence of trojan backdoors,
nor restore high-fidelity trojan backdoor images, especially when the triggers
pertaining to the trojan vary in size, shape and position. In this work, we
propose TABOR, a new trojan detection technique. Conceptually, it formalizes a
trojan detection task as a non-convex optimization problem, and the detection
of a trojan backdoor as the task of resolving the optimization through an
objective function. Different from the existing technique also modeling trojan
detection as an optimization problem, TABOR designs a new objective
function--under the guidance of explainable AI techniques as well as
heuristics--that could guide optimization to identify a trojan backdoor in a
more effective fashion. In addition, TABOR defines a new metric to measure the
quality of a trojan backdoor identified. Using an anomaly detection method, we
show the new metric could better facilitate TABOR to identify intentionally
injected triggers in an infected model and filter out false alarms......
