Title: Robust and Imperceptible Adversarial Attacks on Capsule Networks,
Abstract: Capsule Networks envision an innovative point of view about the
representation of the objects in the brain and preserve the hierarchical
spatial relationships between them. This type of networks exhibits a huge
potential for several Machine Learning tasks like image classification, while
outperforming Convolutional Neural Networks (CNNs). A large body of work has
explored adversarial examples for CNNs, but their efficacy to Capsule Networks
is not well explored. In our work, we study the vulnerabilities in Capsule
Networks to adversarial attacks. These perturbations, added to the test inputs,
are small and imperceptible to humans, but fool the network to mis-predict. We
propose a greedy algorithm to automatically generate targeted imperceptible
adversarial examples in a black-box attack scenario. We show that this kind of
attacks, when applied to the German Traffic Sign Recognition Benchmark (GTSRB),
mislead Capsule Networks. Moreover, we apply the same kind of adversarial
attacks to a 9-layer CNN and analyze the outcome, compared to the Capsule
Networks to study their differences / commonalities.