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.

本研究提出一种名为 DeepCloak 的防御性机制，通过识别和删除深度神经网络模型中多余的特征，限制攻击者生成对抗样本所需使用的维度，增强模型的抗干扰性能。实验结果表明，DeepCloak 能够提高最先进的深度神经网络模型对抗样本的性能。