Generating 3D Adversarial Point Clouds (1809.07016v4)

Abstract: Deep neural networks are known to be vulnerable to adversarial examples which are carefully crafted instances to cause the models to make wrong predictions. While adversarial examples for 2D images and CNNs have been extensively studied, less attention has been paid to 3D data such as point clouds. Given many safety-critical 3D applications such as autonomous driving, it is important to study how adversarial point clouds could affect current deep 3D models. In this work, we propose several novel algorithms to craft adversarial point clouds against PointNet, a widely used deep neural network for point cloud processing. Our algorithms work in two ways: adversarial point perturbation and adversarial point generation. For point perturbation, we shift existing points negligibly. For point generation, we generate either a set of independent and scattered points or a small number (1-3) of point clusters with meaningful shapes such as balls and airplanes which could be hidden in the human psyche. In addition, we formulate six perturbation measurement metrics tailored to the attacks in point clouds and conduct extensive experiments to evaluate the proposed algorithms on the ModelNet40 3D shape classification dataset. Overall, our attack algorithms achieve a success rate higher than 99% for all targeted attacks

Generating 3D Adversarial Point Clouds

The paper "Generating 3D Adversarial Point Clouds" presents an in-depth exploration into the vulnerabilities of 3D deep learning models, particularly focusing on PointNet, against adversarial attacks. While substantial research has been conducted in generating adversarial examples for 2D images, this paper extends those concepts into the 3D space, targeting point cloud data—critical for applications like autonomous driving.

Key Contributions and Methodology

The authors introduce novel algorithms to manufacture adversarial point clouds via two primary methods: adversarial point perturbation and adversarial point generation. In point perturbation, small shifts are applied to existing points, whereas point generation involves crafting new points or clusters that consolidate as independent points or form discrete, meaningful shapes such as spheres or airplane-like objects. These shapes are strategically placed so as to remain imperceptible or appear benign to human observers.

The research introduces six tailored perturbation metrics to evaluate the adversarial impact on point clouds. The algorithms were tested extensively on the ModelNet40 dataset, where the proposed methods demonstrated attack success rates exceeding 99% for all targeted attacks.

Experimental Results and Observations

The experiments underscore the susceptibility of PointNet, revealing the potential for both minor perturbations and point additions to effectuate misclassification with high success rates. Notably, the adversarial attacks—whether through perturbation or generation—expose weaknesses in the model's robustness. The success rate of adversarial point perturbation is consistent across variants with a 100% success rate demonstrated under adversarial point perturbation strategies with acceptable perturbation budgets.

For point generation, different modes of attack allow for different performance characteristics. The generation of independent points and structured clusters such as adversarial objects demonstrated varying levels of susceptibility, with attacks including three point clusters achieving a success rate of 99.3%, regardless of configuration intricacies.

Theoretical and Practical Implications

The results of this research raise significant implications for 3D neural network applications, particularly where safety is critical, such as autonomous driving systems. The demonstrated vulnerability of PointNet suggests a need for more robust model architectures or defensive strategies. Furthermore, this paper opens up a discussion about the robustness of other 3D deep learning models and the generalizability of such adversarial attack methodologies across different neural architectures.

Future Prospects

Future research may venture into developing defensive mechanisms that incorporate adversarial training strategies or hybrid models that draw on both CNN and PointNet architectures to fortify defenses against adversarial attacks. Additionally, expanding the understanding of adversarial transferability to other models like PointNet++ and DGCNN warrants further investigation. Exploring the unique characteristics that underpin the robustness of 3D models might offer insights into crafting more resilient systems.

Conclusion

This paper represents a significant stride towards understanding and addressing adversarial vulnerabilities in 3D deep learning models. By extending adversarial attack strategies into the third dimension, it highlights a pivotal blind spot in current security paradigms and sets the stage for developing more secure and resilient models in the field of 3D data processing and beyond. The release of sample code and data supports further exploration and development in this critical area of research.