论文标题
防御点：保护角网免受对抗攻击
Defense-PointNet: Protecting PointNet Against Adversarial Attacks
论文作者
论文摘要
尽管在广泛的任务中表现出色，但神经网络已被证明容易受到对抗性攻击的影响。许多作品专注于对2D图像的对抗攻击和防御，但很少关注3D点云。在本文中，我们的目标是增强PointNet的对抗性鲁棒性，这是3D点云最广泛使用的模型之一。我们在3D点云上应用快速梯度标志攻击方法（FGSM），发现FGSM不仅可以用于生成对抗图像，还可以生成对抗点云。为了最大程度地减少点网对对抗攻击的脆弱性，我们提出了防御点网。我们将模型与两种基线方法进行比较，并表明防御点可显着提高网络对对抗样本的鲁棒性。
Despite remarkable performance across a broad range of tasks, neural networks have been shown to be vulnerable to adversarial attacks. Many works focus on adversarial attacks and defenses on 2D images, but few focus on 3D point clouds. In this paper, our goal is to enhance the adversarial robustness of PointNet, which is one of the most widely used models for 3D point clouds. We apply the fast gradient sign attack method (FGSM) on 3D point clouds and find that FGSM can be used to generate not only adversarial images but also adversarial point clouds. To minimize the vulnerability of PointNet to adversarial attacks, we propose Defense-PointNet. We compare our model with two baseline approaches and show that Defense-PointNet significantly improves the robustness of the network against adversarial samples.
