  Deep neural networks are known to have security issues. One particular threat
is the Trojan attack. It occurs when the attackers stealthily manipulate the
model's behavior through Trojaned training samples, which can later be
exploited.
  Guided by basic neuroscientific principles we discover subtle -- yet critical
-- structural deviation characterizing Trojaned models. In our analysis we use
topological tools. They allow us to model high-order dependencies in the
networks, robustly compare different networks, and localize structural
abnormalities. One interesting observation is that Trojaned models develop
short-cuts from input to output layers.
  Inspired by these observations, we devise a strategy for robust detection of
Trojaned models. Compared to standard baselines it displays better performance
on multiple benchmarks.
