  Neural networks have achieved state-of-the-art performance in solving many
problems, including many applications in safety/security-critical systems.
Researchers also discovered multiple security issues associated with neural
networks. One of them is backdoor attacks, i.e., a neural network may be
embedded with a backdoor such that a target output is almost always generated
in the presence of a trigger. Existing defense approaches mostly focus on
detecting whether a neural network is 'backdoored' based on heuristics, e.g.,
activation patterns. To the best of our knowledge, the only line of work which
certifies the absence of backdoor is based on randomized smoothing, which is
known to significantly reduce neural network performance. In this work, we
propose an approach to verify whether a given neural network is free of
backdoor with a certain level of success rate. Our approach integrates
statistical sampling as well as abstract interpretation. The experiment results
show that our approach effectively verifies the absence of backdoor or
generates backdoor triggers.
