# LPF-Defense: 3D Adversarial Defense based on Frequency Analysis

arXiv: https://arxiv.org/abs/2202.11287 · HTML: https://mlsanity.com/paper/2202.11287/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 66 references that are in the map.
- First posted on arXiv 23 Feb 2022.

## Builds on (most cited first, 30 of 40)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Adversarial Examples Are Not Bugs, They Are Features](https://mlsanity.com/paper/1905.02175/) (2019)
- [On Adaptive Attacks to Adversarial Example Defenses](https://mlsanity.com/paper/2002.08347/) (2020)
- [Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data](https://mlsanity.com/paper/1908.04616/) (2019)
- [Spherical CNNs](https://mlsanity.com/paper/1801.10130/) (2018)
- [High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks](https://mlsanity.com/paper/1905.13545/) (2019)
- [A Fourier Perspective on Model Robustness in Computer Vision](https://mlsanity.com/paper/1906.08988/) (2019)
- [Generating 3D Adversarial Point Clouds](https://mlsanity.com/paper/1809.07016/) (2018)
- [PointCloud Saliency Maps](https://mlsanity.com/paper/1812.01687/) (2018)
- [Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers](https://mlsanity.com/paper/1901.03006/) (2019)
- [AdvPC: Transferable Adversarial Perturbations on 3D Point Clouds](https://mlsanity.com/paper/1912.00461/) (2019)
- [Low Frequency Adversarial Perturbation](https://mlsanity.com/paper/1809.08758/) (2018)
- [Adversarial Attack and Defense on Point Sets](https://mlsanity.com/paper/1902.10899/) (2019)
- [Effective Rotation-invariant Point CNN with Spherical Harmonics kernels](https://mlsanity.com/paper/1906.11555/) (2019)
- [On the Effectiveness of Low Frequency Perturbations](https://mlsanity.com/paper/1903.00073/) (2019)
- [AdvDrop: Adversarial Attack to DNNs by Dropping Information](https://mlsanity.com/paper/2108.09034/) (2021)
- [Robustness of 3D Deep Learning in an Adversarial Setting](https://mlsanity.com/paper/1904.00923/) (2019)
- [LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud-based Deep Networks](https://mlsanity.com/paper/2011.00566/) (2020)
- [PointGuard: Provably Robust 3D Point Cloud Classification](https://mlsanity.com/paper/2103.03046/) (2021)
- [IF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration](https://mlsanity.com/paper/2010.05272/) (2020)
- [Minimal Adversarial Examples for Deep Learning on 3D Point Clouds](https://mlsanity.com/paper/2008.12066/) (2020)
- [Imperceptible Transfer Attack and Defense on 3D Point Cloud Classification](https://mlsanity.com/paper/2111.10990/) (2021)
- [Hold me tight! Influence of discriminative features on deep network boundaries](https://mlsanity.com/paper/2002.06349/) (2020)
- [Towards Frequency-Based Explanation for Robust CNN](https://mlsanity.com/paper/2005.03141/) (2020)

## Built on by (most cited first, 10 of 10)

- [FrePolad: Frequency-Rectified Point Latent Diffusion for Point Cloud Generation](https://mlsanity.com/paper/2311.12090/) (2023)
- [Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Survey](https://mlsanity.com/paper/2307.00309/) (2023)
- [iBA: Backdoor Attack on 3D Point Cloud via Reconstructing Itself](https://mlsanity.com/paper/2403.05847/) (2024)
- [SAGA: Spectral Adversarial Geometric Attack on 3D Meshes](https://mlsanity.com/paper/2211.13775/) (2022)
- [Robustifying Point Cloud Networks by Refocusing](https://mlsanity.com/paper/2308.05525/) (2023)
- [PointACL:Adversarial Contrastive Learning for Robust Point Clouds Representation under Adversarial Attack](https://mlsanity.com/paper/2209.06971/) (2022)
- [Invariance-powered Trustworthy Defense via Remove Then Restore](https://mlsanity.com/paper/2402.00304/) (2024)
- [Robust Unsupervised Domain Adaptation for 3D Point Cloud Segmentation Under Source Adversarial Attacks](https://mlsanity.com/paper/2504.01659/) (2025)
- [KNN-Defense: Defense against 3D Adversarial Point Clouds using Nearest-Neighbor Search](https://mlsanity.com/paper/2506.06906/) (2025)
- [Defending against Patch-Based and Texture-Based Adversarial Attacks with Spectral Decomposition](https://mlsanity.com/paper/2604.10715/) (2026)

## Abstract (arXiv)

Although 3D point cloud classification has recently been widely deployed in different application scenarios, it is still very vulnerable to adversarial attacks. This increases the importance of robust training of 3D models in the face of adversarial attacks. Based on our analysis on the performance of existing adversarial attacks, more adversarial perturbations are found in the mid and high-frequency components of input data. Therefore, by suppressing the high-frequency content in the training phase, the models robustness against adversarial examples is improved. Experiments showed that the proposed defense method decreases the success rate of six attacks on PointNet, PointNet++ ,, and DGCNN models. In particular, improvements are achieved with an average increase of classification accuracy by 3.8 % on drop100 attack and 4.26 % on drop200 attack compared to the state-of-the-art methods. The method also improves models accuracy on the original dataset compared to other available methods.