Neural networks are vulnerable to adversarial examples, which poses a threat
to their application in security sensitive systems. We propose high-level
representation guided denoiser (HGD) as a defense for image classification.
Standard denoiser suffers from the error amplification effect, in which small
residual adversarial noise is progressively amplified and leads to wrong
classifications. HGD overcomes this problem by using a loss function defined as
the difference between the target model's outputs activated by the clean image
and denoised image. Compared with ensemble adversarial training which is the
state-of-the-art defending method on large images, HGD has three advantages.
First, with HGD as a defense, the target model is more robust to either
white-box or black-box adversarial attacks. Second, HGD can be trained on a
small subset of the images and generalizes well to other images and unseen
classes. Third, HGD can be transferred to defend models other than the one
guiding it. In NIPS competition on defense against adversarial attacks, our HGD
solution won the first place and outperformed other models by a large margin.

提出一种基于高级表示引导去噪者的防御方法（HGD），用于图像分类，并证明该方法在防御白盒和黑盒对抗攻击中表现更加稳健，并可适用于其他模型。

使用高层次表征引导去噪器抵御对抗攻击

Defense against Adversarial Attacks Using High-Level Representation  Guided Denoiser

Following the recent adoption of deep neural networks (DNN) accross a wide
range of applications, adversarial attacks against these models have proven to
be an indisputable threat. Adversarial samples are crafted with a deliberate
intention of undermining a system. In the case of DNNs, the lack of better
understanding of their working has prevented the development of efficient
defenses. In this paper, we propose a new defense method based on practical
observations which is easy to integrate into models and performs better than
state-of-the-art defenses. Our proposed solution is meant to reinforce the
structure of a DNN, making its prediction more stable and less likely to be
fooled by adversarial samples. We conduct an extensive experimental study
proving the efficiency of our method against multiple attacks, comparing it to
numerous defenses, both in white-box and black-box setups. Additionally, the
implementation of our method brings almost no overhead to the training
procedure, while maintaining the prediction performance of the original model
on clean samples.

本文提出了一种基于实践观察的新的防御方法，旨在强化深度神经网络的结构，提高其预测稳定性，从而更难受到针对性攻击，并在多种攻击实验中证明了该方法的有效性，相比其他防御方法具有更好的表现，而且在训练过程中的开销几乎可以忽略不计。