  State-of-the-art deep neural networks (DNNs) are highly effective in solving
many complex real-world problems. However, these models are vulnerable to
adversarial perturbation attacks, and despite the plethora of research in this
domain, to this day, adversaries still have the upper hand in the cat and mouse
game of adversarial example generation methods vs. detection and prevention
methods. In this research, we present a novel detection method that uses
Shapley Additive Explanations (SHAP) values computed for the internal layers of
a DNN classifier to discriminate between normal and adversarial inputs. We
evaluate our method by building an extensive dataset of adversarial examples
over the popular CIFAR-10 and MNIST datasets, and training a neural
network-based detector to distinguish between normal and adversarial inputs. We
evaluate our detector against adversarial examples generated by diverse
state-of-the-art attacks and demonstrate its high detection accuracy and strong
generalization ability to adversarial inputs generated with different attack
methods.
