Title: DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples,
Abstract: Recent studies have shown that deep neural networks (DNN) are vulnerable to
adversarial samples: maliciously-perturbed samples crafted to yield incorrect
model outputs. Such attacks can severely undermine DNN systems, particularly in
security-sensitive settings. It was observed that an adversary could easily
generate adversarial samples by making a small perturbation on irrelevant
feature dimensions that are unnecessary for the current classification task. To
overcome this problem, we introduce a defensive mechanism called DeepCloak. By
identifying and removing unnecessary features in a DNN model, DeepCloak limits
the capacity an attacker can use generating adversarial samples and therefore
increase the robustness against such inputs. Comparing with other defensive
approaches, DeepCloak is easy to implement and computationally efficient.
Experimental results show that DeepCloak can increase the performance of
state-of-the-art DNN models against adversarial samples.