  It is well established that neural networks are vulnerable to adversarial
examples, which are almost imperceptible on human vision and can cause the deep
models misbehave. Such phenomenon may lead to severely inestimable consequences
in the safety and security critical applications. Existing defenses are trend
to harden the robustness of models against adversarial attacks, e.g.,
adversarial training technology. However, these are usually intractable to
implement due to the high cost of re-training and the cumbersome operations of
altering the model architecture or parameters. In this paper, we discuss the
saliency map method from the view of enhancing model interpretability, it is
similar to introducing the mechanism of the attention to the model, so as to
comprehend the progress of object identification by the deep networks. We then
propose a novel method combined with additional noises and utilize the
inconsistency strategy to detect adversarial examples. Our experimental results
of some representative adversarial attacks on common datasets including
ImageNet and popular models show that our method can detect all the attacks
with high detection success rate effectively. We compare it with the existing
state-of-the-art technique, and the experiments indicate that our method is
more general.
