IF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration (2010.05272v3)

Abstract: 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 https://github.com/Wuziyi616/IF-Defense.

IF-Defense: Emerging Strategies for Mitigating 3D Adversarial Attacks

The paper "IF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration," introduces a novel methodology for defending deep neural networks (DNNs) against adversarial attacks targeting 3D point clouds. The research addresses the vulnerability of point cloud networks, which have demonstrated substantial progress in numerous applications but remain susceptible to adversarial manipulations.

Overview of 3D Adversarial Attacks

The authors classify adversarial attacks on 3D point clouds into two primary categories: point perturbations and surface distortions. Point perturbations adjust the local point distribution, often moving points off the surface or altering their sampling pattern. Surface distortions, on the other hand, cause drastic changes in the geometric structure by either removing parts or altering the shape of the point cloud. These adversaries can significantly impact the performance of models like PointNet and its derivatives, necessitating robust defense mechanisms.

IF-Defense System

The proposed IF-Defense framework tackles both point perturbations and surface distortions by restoring the attacked point clouds to their clean counterparts. This restoration leverages both geometry-aware and distribution-aware constraints. The geometry-aware component utilizes implicit functions to reconstruct the point cloud surface, while the distribution-aware component ensures the corrected points are evenly distributed. The implicit function used, specifically Occupancy Networks (ONet) and their variant Convolutional Occupancy Networks (ConvONet), allows for an effective recovery of original data characteristics, even from sparse or partial data.

Experimental Results

The authors report state-of-the-art defense performance of IF-Defense against several adversarial attacks, including point perturbation, salient point dropping, LG-GAN, and AdvPC attacks, tested across multiple architectures like PointNet, PointNet++, DGCNN, PointConv, and RS-CNN. Notably, the IF-Defense framework considerably improves classification accuracy over existing countermeasures. For example, it achieved a 20.02% improvement in classification accuracy against salient point dropping attacks on PointNet compared to previous defenses.

Implications and Future Directions

The strong defense capabilities of IF-Defense suggest significant potential for practical applications, especially in safety-critical fields such as autonomous driving and robotics, where adversarial robustness is paramount. The utilization of implicit function networks marks a pivotal advancement in defending against 3D adversarial attacks and could influence future work regarding the adaptation of generative models for enhanced robustness against adversarial inputs. Future explorations may focus on optimizing the computational efficiency of the IF-Defense framework and broadening its applicability to other modalities and more complex point cloud data.

Overall, this paper contributes crucial insights and methods to the field of adversarial learning, particularly in the context of 3D data, providing a robust foundation for future research into secure and resilient AI systems.