Automatic speaker verification (ASV) is one of the core technologies in
biometric identification. With the ubiquitous usage of ASV systems in
safety-critical applications, more and more malicious attackers attempt to
launch adversarial attacks at ASV systems. In the midst of the arms race
between attack and defense in ASV, how to effectively improve the robustness of
ASV against adversarial attacks remains an open question. We note that the
self-supervised learning models possess the ability to mitigate superficial
perturbations in the input after pretraining. Hence, with the goal of effective
defense in ASV against adversarial attacks, we propose a standard and
attack-agnostic method based on cascaded self-supervised learning models to
purify the adversarial perturbations. Experimental results demonstrate that the
proposed method achieves effective defense performance and can successfully
counter adversarial attacks in scenarios where attackers may either be aware or
unaware of the self-supervised learning models.

通过使用级联自监督学习模型可在 ASV 中对抗对抗攻击，并提高自身的鲁棒性。实验结果表明，该方法能在攻击者知道或不知道自监督模型的情况下有效地抵御攻击，提高了防御性能。

级联自监督学习模型对自动说话者验证的对抗性防御

Adversarial defense for automatic speaker verification by cascaded  self-supervised learning models

Point cloud is an important 3D data representation widely used in many
essential applications. Leveraging deep neural networks, recent works have
shown great success in processing 3D point clouds. However, those deep neural
networks are vulnerable to various 3D adversarial attacks, which can be
summarized as two primary types: point perturbation that affects local point
distribution, and surface distortion that causes dramatic changes in geometry.
In this paper, we simultaneously address both the aforementioned attacks by
learning to restore the clean point clouds from the attacked ones. More
specifically, we propose an IF-Defense framework to directly optimize the
coordinates of input points with geometry-aware and distribution-aware
constraints. The former aims to recover the surface of point cloud through
implicit function, while the latter encourages evenly-distributed points. Our
experimental results show that IF-Defense achieves the state-of-the-art defense
performance against existing 3D adversarial attacks on PointNet, PointNet++,
DGCNN, PointConv and RS-CNN. For example, compared with previous methods,
IF-Defense presents 20.02% improvement in classification accuracy against
salient point dropping attack and 16.29% against LG-GAN attack on PointNet. Our
code is available at this https URL

本文提出了 IF-Defense 框架，通过同时优化输入点的坐标，并设置几何和分布约束来恢复被攻击的点云，在 PointNet，PointNet ++，DGCNN，PointConv 和 RS-CNN 上实现了现有 3D 对抗攻击的最先进防御表现。