Adversarial attacks hamper the functionality and accuracy of Deep Neural
Networks (DNNs) by meddling with subtle perturbations to their this http URL this
work, we propose a new Mask-based Adversarial Defense scheme (MAD) for DNNs to
mitigate the negative effect from adversarial attacks. To be precise, our
method promotes the robustness of a DNN by randomly masking a portion of
potential adversarial images, and as a result, the %classification result
output of the DNN becomes more tolerant to minor input perturbations. Compared
with existing adversarial defense techniques, our method does not need any
additional denoising structure, nor any change to a DNN's design. We have
tested this approach on a collection of DNN models for a variety of data sets,
and the experimental results confirm that the proposed method can effectively
improve the defense abilities of the DNNs against all of the tested adversarial
attack methods. In certain scenarios, the DNN models trained with MAD have
improved classification accuracy by as much as 20% to 90% compared to the
original models that are given adversarial inputs.

本文介绍了一种新的基于掩码的防御策略（MAD），使用此方法可提高深度神经网络模型对低强度的输入扰动的防御能力，相比现有的防御技术该方法不需要任何附加去噪结构或对模型设计的改变，并且实验证明该方法可以对多种对抗性攻击的深度神经网络模型的防御能力提高 20% 至 90% 的分类精度。