Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Hiding Imperceptible Noise in Curvature-Aware Patches for 3D Point Cloud Attack

  • Conference paper
  • First Online:
Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15088))

Included in the following conference series:

Abstract

With the maturity of depth sensors, point clouds have received increasing attention in various 3D safety-critical applications, while deep point cloud learning models have been shown to be vulnerable to adversarial attacks. Most existing 3D attackers rely on implicit global distance losses to perturb whole points, failing to restrict the proper 3D geometry as point clouds are highly structured. To this end, in this paper, we propose a novel Wavelet Patches Attack (WPA), which leverages local spectral attributes to identify curvature-aware patches for hiding imperceptible perturbations aligned with their local geometric characteristics. Specifically, WPA first transforms the point cloud into the spectral domain using a wavelet operator, obtaining potential geometric structures in different local regions. Each wavelet corresponds to different curvature contexts of local points. Then, by decomposing the 3D object with different curvature-aware levels through the wavelet coefficients, we can perceive the local geometric characteristics and get various curvature-consistent patches. At last, based on the curvature variations of patches, WPA introduces two-type perturbations along the tangent plane and normal vector direction to hide imperceptible noise in slow- and fast-variation patches for preserving the geometric-sensitive local characteristics of smoothness and sharpness, respectively. Experiments demonstrate the superior imperceptibility of our attack method, achieving favorable results on existing 3D classification models while exhibiting robust resistance to various defense mechanisms.

M. Yang and D. Liu contributed equally to this paper.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: 2017 IEEE Symposium on Security and Privacy (SP), pp. 39–57 (2017)

    Google Scholar 

  2. Chen, S., Tian, D., Feng, C., Vetro, A., Kovačević, J.: Fast resampling of three-dimensional point clouds via graphs. IEEE Trans. Signal Process. 66(3), 666–681 (2017)

    Article  MathSciNet  Google Scholar 

  3. Chen, X., Ma, H., Wan, J., Li, B., Xia, T.: Multi-view 3D object detection network for autonomous driving. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR), pp. 1907–1915 (2017)

    Google Scholar 

  4. Dong, Y., et al.: Boosting adversarial attacks with momentum. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9185–9193 (2018)

    Google Scholar 

  5. Fan, H., Su, H., Guibas, L.J.: A point set generation network for 3D object reconstruction from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 605–613 (2017)

    Google Scholar 

  6. Fang, X., et al..: Fewer steps, better performance: Efficient cross-modal clip trimming for video moment retrieval using language. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 1735–1743 (2024)

    Google Scholar 

  7. Fang, X., Liu, D., Zhou, P., Hu, Y.: Multi-modal cross-domain alignment network for video moment retrieval. IEEE Trans. Multimedia 25, 7517–7532 (2022)

    Article  Google Scholar 

  8. Fang, X., Liu, D., Zhou, P., Nan, G.: You can ground earlier than see: an effective and efficient pipeline for temporal sentence grounding in compressed videos. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2448–2460 (2023)

    Google Scholar 

  9. Fang, X., et al.: Rethinking weakly-supervised video temporal grounding from a game perspective. In: European Conference on Computer Vision. Springer (2024)

    Google Scholar 

  10. Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)

  11. Hamdi, A., Rojas, S., Thabet, A., Ghanem, B.: AdvPC: transferable adversarial perturbations on 3D point clouds. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12357, pp. 241–257. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58610-2_15

    Chapter  Google Scholar 

  12. Hammond, D.K., Vandergheynst, P., Gribonval, R.: Wavelets on graphs via spectral graph theory. Appl. Comput. Harmonic Anal. 30(2), 129–150 (2011)

    Article  MathSciNet  Google Scholar 

  13. Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., Stuetzle, W.: Surface reconstruction from unorganized points. In: Proceedings of the 19th Annual Conference on Computer Graphics and Interactive Techniques, pp. 71–78 (1992)

    Google Scholar 

  14. Hu, Q., Liu, D., Hu, W.: Exploring the devil in graph spectral domain for 3d point cloud attacks. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13663, pp. 229–248. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20062-5_14

    Chapter  Google Scholar 

  15. Hu, W., Pang, J., Liu, X., Tian, D., Lin, C.W., Vetro, A.: Graph signal processing for geometric data and beyond: theory and applications. IEEE Trans. Multimedia 24, 3961–3977 (2021)

    Article  Google Scholar 

  16. Huang, Q., Dong, X., Chen, D., Zhou, H., Zhang, W., Yu, N.: Shape-invariant 3D adversarial point clouds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15335–15344 (2022)

    Google Scholar 

  17. Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.J.: Comparing images using the hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 15(9), 850–863 (1993)

    Article  Google Scholar 

  18. Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial machine learning at scale. arXiv preprint arXiv:1611.01236 (2016)

  19. Liu, D., Hu, W.: Imperceptible transfer attack and defense on 3d point cloud classification. IEEE Trans. Pattern Anal. Mach. Intell. 45(4), 4727–4746 (2022)

    Google Scholar 

  20. Liu, D., Hu, W.: Explicitly perceiving and preserving the local geometric structures for 3D point cloud attack. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 3576–3584 (2024)

    Google Scholar 

  21. Liu, D., Hu, W., Li, X.: Point cloud attacks in graph spectral domain: when 3D geometry meets graph signal processing. IEEE Trans. Pattern Anal. Mach. Intell. (2023)

    Google Scholar 

  22. Liu, D., Hu, W., Li, X.: Robust geometry-dependent attack for 3D point clouds. IEEE Trans. Multimedia (2023)

    Google Scholar 

  23. Liu, D., Yu, R., Su, H.: Extending adversarial attacks and defenses to deep 3D point cloud classifiers. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 2279–2283 (2019)

    Google Scholar 

  24. Liu, M., Li, X., Ling, Z., Li, Y., Su, H.: Frame mining: a free lunch for learning robotic manipulation from 3D point clouds. In: Conference on Robot Learning, pp. 527–538. PMLR (2023)

    Google Scholar 

  25. Ma, C., Meng, W., Wu, B., Xu, S., Zhang, X.: Efficient joint gradient based attack against SOR defense for 3D point cloud classification. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 1819–1827 (2020)

    Google Scholar 

  26. Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083 (2017)

  27. Moosavi-Dezfooli, S.M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1765–1773 (2017)

    Google Scholar 

  28. Moosavi-Dezfooli, S.M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2574–2582 (2016)

    Google Scholar 

  29. Mustafa, A., Khan, S.H., Hayat, M., Goecke, R., Shen, J., Shao, L.: Deeply supervised discriminative learning for adversarial defense. IEEE Trans. Pattern Anal. Mach. Intell. 43(9), 3154–3166 (2020)

    Article  Google Scholar 

  30. Ortega, A., Frossard, P., Kovačević, J., Moura, J.M., Vandergheynst, P.: Graph signal processing: overview, challenges, and applications. Proc. IEEE 106(5), 808–828 (2018)

    Article  Google Scholar 

  31. Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 652–660 (2017)

    Google Scholar 

  32. Ramasinghe, S., Khan, S., Barnes, N., Gould, S.: Spectral-gans for high-resolution 3d point-cloud generation. In: RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 8169–8176. IEEE (2020)

    Google Scholar 

  33. Rosman, G., Dubrovina, A., Kimmel, R.: Patch-collaborative spectral point-cloud denoising. In: Computer Graphics Forum, vol. 32, pp. 1–12. Wiley Online Library (2013)

    Google Scholar 

  34. Singh, S.P., Wang, L., Gupta, S., Goli, H., Padmanabhan, P., Gulyás, B.: 3D deep learning on medical images: a review. Sensors 20(18), 5097 (2020)

    Article  Google Scholar 

  35. Szegedy, C., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)

  36. Tang, K., et al.: Manifold constraints for imperceptible adversarial attacks on point clouds. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 5127–5135 (2024)

    Google Scholar 

  37. Tao, Y., Liu, D., Zhou, P., Xie, Y., Du, W., Hu, W.: 3dhacker: spectrum-based decision boundary generation for hard-label 3D point cloud attack. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14340–14350 (2023)

    Google Scholar 

  38. Tsai, T., Yang, K., Ho, T.Y., Jin, Y.: Robust adversarial objects against deep learning models. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 954–962 (2020)

    Google Scholar 

  39. Tu, C.C., et al.: Autozoom: Autoencoder-based zeroth order optimization method for attacking black-box neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 742–749 (2019)

    Google Scholar 

  40. Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph Dfor learning on point clouds. ACM Trans. Graphics (TOG) 38(5), 1–12 (2019)

    Article  Google Scholar 

  41. Wen, Y., Lin, J., Chen, K., Chen, C.P., Jia, K.: Geometry-aware generation of adversarial point clouds. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) (2020)

    Google Scholar 

  42. Wicker, M., Kwiatkowska, M.: Robustness of 3d deep learning in an adversarial setting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11767–11775 (2019)

    Google Scholar 

  43. Wu, W., Qi, Z., Fuxin, L.: PointConv: deep convolutional networks on 3d point clouds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9621–9630 (2019)

    Google Scholar 

  44. Wu, Z., et al.: 3D shapenets: a deep representation for volumetric shapes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1912–1920 (2015)

    Google Scholar 

  45. Wu, Z., Duan, Y., Wang, H., Fan, Q., Guibas, L.J.: If-defense: 3D adversarial point cloud defense via implicit function based restoration. arXiv preprint arXiv:2010.05272 (2020)

  46. Xiang, C., Qi, C.R., Li, B.: Generating 3D adversarial point clouds. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9136–9144 (2019)

    Google Scholar 

  47. Xiang, T., Zhang, C., Song, Y., Yu, J., Cai, W.: Walk in the cloud: learning curves for point clouds shape analysis. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 915–924 (2021)

    Google Scholar 

  48. Yi, L., et al.: A scalable active framework for region annotation in 3D shape collections. ACM Trans. Graphics (ToG) 35(6), 1–12 (2016)

    Article  Google Scholar 

  49. Zhang, Q., Yang, J., Fang, R., Ni, B., Liu, J., Tian, Q.: Adversarial attack and defense on point sets. arXiv preprint arXiv:1902.10899 (2019)

  50. Zhang, S., Cui, S., Ding, Z.: Hypergraph spectral analysis and processing in 3D point cloud. IEEE Trans. Image Process. 30, 1193–1206 (2020)

    Article  MathSciNet  Google Scholar 

  51. Zhang, Y., Liang, G., Salem, T., Jacobs, N.: Defense-pointnet: protecting pointnet against adversarial attacks. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 5654–5660 (2019)

    Google Scholar 

  52. Zhao, Y., Wu, Y., Chen, C., Lim, A.: On isometry robustness of deep 3D point cloud models under adversarial attacks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1201–1210 (2020)

    Google Scholar 

  53. Zheng, T., Chen, C., Yuan, J., Li, B., Ren, K.: Pointcloud saliency maps. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1598–1606 (2019)

    Google Scholar 

  54. Zhou, H., et al.: LG-GAN: label guided adversarial network for flexible targeted attack of point cloud based deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10356–10365 (2020)

    Google Scholar 

  55. Zhou, H., Chen, K., Zhang, W., Fang, H., Zhou, W., Yu, N.: Dup-net: denoiser and upsampler network for 3D adversarial point clouds defense. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1961–1970 (2019)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Mingyu Yang & Pan Zhou

  2. Peking University, Beijing, China

    Daizong Liu

  3. Guangzhou University, Guangzhou, China

    Keke Tang

  4. Shanghai Jiao Tong University, Shanghai, China

    Lixing Chen

  5. Shenzhen University, Shenzhen, China

    Junyang Chen

Authors
  1. Mingyu Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daizong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Keke Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lixing Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Junyang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Pan Zhou or Lixing Chen .

Editor information

Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Hessen, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 2633 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yang, M., Liu, D., Tang, K., Zhou, P., Chen, L., Chen, J. (2025). Hiding Imperceptible Noise in Curvature-Aware Patches for 3D Point Cloud Attack. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15088. Springer, Cham. https://doi.org/10.1007/978-3-031-73404-5_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-73404-5_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-73403-8

  • Online ISBN: 978-3-031-73404-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation