  We present a novel methodology for neural network backdoor attacks. Unlike
existing training-time attacks where the Trojaned network would respond to the
Trojan trigger after training, our approach inserts a Trojan that will remain
dormant until it is activated. The activation is realized through a specific
perturbation to the network's weight parameters only known to the attacker. Our
analysis and the experimental results demonstrate that dormant Trojaned
networks can effectively evade detection by state-of-the-art backdoor detection
methods.
