# LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud-based Deep Networks

arXiv: https://arxiv.org/abs/2011.00566 · HTML: https://mlsanity.com/paper/2011.00566/

## Key facts

- Cited by 55 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 1 Nov 2020.

## Built on by (most cited first, 30 of 55)

- [Advances in adversarial attacks and defenses in computer vision: A survey](https://mlsanity.com/paper/2108.00401/) (2021)
- [PointCutMix: Regularization Strategy for Point Cloud Classification](https://mlsanity.com/paper/2101.01461/) (2021)
- [PointGuard: Provably Robust 3D Point Cloud Classification](https://mlsanity.com/paper/2103.03046/) (2021)
- [Shape-invariant 3D Adversarial Point Clouds](https://mlsanity.com/paper/2203.04041/) (2022)
- [Imperceptible Transfer Attack and Defense on 3D Point Cloud Classification](https://mlsanity.com/paper/2111.10990/) (2021)
- [3D Adversarial Attacks Beyond Point Cloud](https://mlsanity.com/paper/2104.12146/) (2021)
- [Fooling LiDAR Perception via Adversarial Trajectory Perturbation](https://mlsanity.com/paper/2103.15326/) (2021)
- [Isometric 3D Adversarial Examples in the Physical World](https://mlsanity.com/paper/2210.15291/) (2022)
- [Hide in Thicket: Generating Imperceptible and Rational Adversarial Perturbations on 3D Point Clouds](https://mlsanity.com/paper/2403.05247/) (2024)
- [Exploring the Devil in Graph Spectral Domain for 3D Point Cloud Attacks](https://mlsanity.com/paper/2202.07261/) (2022)
- [Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective](https://mlsanity.com/paper/2302.09457/) (2023)
- [A Survey of Robustness and Safety of 2D and 3D Deep Learning Models Against Adversarial Attacks](https://mlsanity.com/paper/2310.00633/) (2023)
- [Boosting 3D Adversarial Attacks with Attacking On Frequency](https://mlsanity.com/paper/2201.10937/) (2022)
- [Robust Structured Declarative Classifiers for 3D Point Clouds: Defending Adversarial Attacks with Implicit Gradients](https://mlsanity.com/paper/2203.15245/) (2022)
- [Point Cloud Attacks in Graph Spectral Domain: When 3D Geometry Meets Graph Signal Processing](https://mlsanity.com/paper/2207.13326/) (2022)
- [PointDP: Diffusion-driven Purification against Adversarial Attacks on 3D Point Cloud Recognition](https://mlsanity.com/paper/2208.09801/) (2022)
- [Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm](https://mlsanity.com/paper/2106.06027/) (2021)
- [Improving Adversarial Robustness of Masked Autoencoders via Test-time Frequency-domain Prompting](https://mlsanity.com/paper/2308.10315/) (2023)
- [LPF-Defense: 3D Adversarial Defense based on Frequency Analysis](https://mlsanity.com/paper/2202.11287/) (2022)
- [PointCAT: Contrastive Adversarial Training for Robust Point Cloud Recognition](https://mlsanity.com/paper/2209.07788/) (2022)
- [GeoPointGAN: Synthetic Spatial Data with Local Label Differential Privacy](https://mlsanity.com/paper/2205.08886/) (2022)
- [PointCA: Evaluating the Robustness of 3D Point Cloud Completion Models Against Adversarial Examples](https://mlsanity.com/paper/2211.12294/) (2022)
- [3DHacker: Spectrum-based Decision Boundary Generation for Hard-label 3D Point Cloud Attack](https://mlsanity.com/paper/2308.07546/) (2023)
- [Ada3Diff: Defending against 3D Adversarial Point Clouds via Adaptive Diffusion](https://mlsanity.com/paper/2211.16247/) (2022)
- [Poisoning MorphNet for Clean-Label Backdoor Attack to Point Clouds](https://mlsanity.com/paper/2105.04839/) (2021)
- [Explainability-Aware One Point Attack for Point Cloud Neural Networks](https://mlsanity.com/paper/2110.04158/) (2021)
- [Invariance-Aware Randomized Smoothing Certificates](https://mlsanity.com/paper/2211.14207/) (2022)
- [The art of defense: letting networks fool the attacker](https://mlsanity.com/paper/2104.02963/) (2021)
- [Adversarial Attack by Limited Point Cloud Surface Modifications](https://mlsanity.com/paper/2110.03745/) (2021)
- [Sample-adaptive Augmentation for Point Cloud Recognition Against Real-world Corruptions](https://mlsanity.com/paper/2309.10431/) (2023)

## Abstract (arXiv)

Deep neural networks have made tremendous progress in 3D point-cloud recognition. Recent works have shown that these 3D recognition networks are also vulnerable to adversarial samples produced from various attack methods, including optimization-based 3D Carlini-Wagner attack, gradient-based iterative fast gradient method, and skeleton-detach based point-dropping. However, after a careful analysis, these methods are either extremely slow because of the optimization/iterative scheme, or not flexible to support targeted attack of a specific category. To overcome these shortcomings, this paper proposes a novel label guided adversarial network (LG-GAN) for real-time flexible targeted point cloud attack. To the best of our knowledge, this is the first generation based 3D point cloud attack method. By feeding the original point clouds and target attack label into LG-GAN, it can learn how to deform the point clouds to mislead the recognition network into the specific label only with a single forward pass. In detail, LGGAN first leverages one multi-branch adversarial network to extract hierarchical features of the input point clouds, then incorporates the specified label information into multiple intermediate features using the label encoder. Finally, the encoded features will be fed into the coordinate reconstruction decoder to generate the target adversarial sample. By evaluating different point-cloud recognition models (e.g., PointNet, PointNet++ and DGCNN), we demonstrate that the proposed LG-GAN can support flexible targeted attack on the fly while guaranteeing good attack performance and higher efficiency simultaneously.