论文标题
3D点云的强大结构化声明性分类器：捍卫具有隐式梯度的对抗攻击
Robust Structured Declarative Classifiers for 3D Point Clouds: Defending Adversarial Attacks with Implicit Gradients
论文作者
论文摘要
3D点云分类（例如PointNet）的深神经网络已被证明容易受到对抗攻击的影响。当前的对手防御者通常会通过重建来确定（攻击）点云，然后将其作为输入喂入分类器。与文献相反，我们提出了一个稳健的结构化声明性分类器的家族，以进行点云分类，其中内部约束优化机制可以通过隐式梯度有效地捍卫对抗性攻击。可以使用双重优化框架制定此类分类器。我们进一步提出了基于可训练的端到端的可训练的终端晶格和2D卷积神经网络（CNNS）中结构化的稀疏编码，即基于结构化的稀疏编码。我们在七个不同的攻击者下展示了ModelNet40和Scannet上最新的强大点云分类性能。例如，在最近的JGBA攻击者下，我们在每个数据集上实现了89.51％和83.16％的测试精度，该数据集的表现优于DUP-NET，而IF-Defense则以PointNet的限制为〜70％。演示代码可从https://zhang-vislab.github.io获得。
Deep neural networks for 3D point cloud classification, such as PointNet, have been demonstrated to be vulnerable to adversarial attacks. Current adversarial defenders often learn to denoise the (attacked) point clouds by reconstruction, and then feed them to the classifiers as input. In contrast to the literature, we propose a family of robust structured declarative classifiers for point cloud classification, where the internal constrained optimization mechanism can effectively defend adversarial attacks through implicit gradients. Such classifiers can be formulated using a bilevel optimization framework. We further propose an effective and efficient instantiation of our approach, namely, Lattice Point Classifier (LPC), based on structured sparse coding in the permutohedral lattice and 2D convolutional neural networks (CNNs) that is end-to-end trainable. We demonstrate state-of-the-art robust point cloud classification performance on ModelNet40 and ScanNet under seven different attackers. For instance, we achieve 89.51% and 83.16% test accuracy on each dataset under the recent JGBA attacker that outperforms DUP-Net and IF-Defense with PointNet by ~70%. Demo code is available at https://zhang-vislab.github.io.
