Adversarial attacks hamper the functionality and accuracy of Deep Neural Networks (DNNs) by meddling with subtle perturbations to their inputs.In 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.

翻译：反向攻击会干扰深神经网络(DNN)的功能和准确性,干扰其投入的微妙扰动。 在这项工作中,我们为DNN提出一个新的基于面具的反向防御计划(MAD),以减轻对抗性攻击的负面影响。准确地说,我们的方法通过随机掩蔽部分潜在的对抗性图像,促进DNN的稳健性,结果导致DNN的%分类结果输出对较小的输入扰动更加宽容。与现有的对抗性防御技术相比,我们的方法不需要任何额外的拆卸结构,也不需要对DNN的设计作任何改动。我们已经在收集各种数据集的DNN模型上测试了这一方法,实验结果证实,拟议的方法可以有效地提高DNN的防御能力,对抗所有经过测试的对抗性攻击方法。在某些情况下,经过MAD培训的D模型提高了分类精度,比最初的敌对性投入模型提高了20%至90%。