Abstract
With the possibility of deceiving deep learning models by appropriately modifying images verified, lots of researches on adversarial attacks and adversarial defenses have been carried out in academia. However, there is few research on adversarial attacks and adversarial defenses of point cloud semantic segmentation models, especially in the field of autonomous driving. The stability and robustness of point cloud semantic segmentation models are our primary concerns in this paper. Aiming at the point cloud segmentation model RangeNet++ in the field of autonomous driving, we propose novel approaches to improve the security and anti-attack capability of the RangeNet++ model. One is to calculate the local geometry that can reflect the surface shape of the point cloud based on the range image. The other is to obtain a general adversarial sample related only to the image itself and closer to the real world based on the range image, then add it into the training set for training. The experimental results show that the proposed approaches can effectively improve the RangeNet + +’s defense ability against adversarial attacks, and meanwhile enhance the RangeNet++ model’s robustness.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Behley, J., et al.: SemanticKITTI: a dataset for semantic scene understanding of LiDAR sequences. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2019)
Milioto, A., Vizzo, I., Behley, J., Stachniss, C.: RangeNet++: fast and accurate LiDAR semantic segmentation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2019)
Weng, H., et al.: Towards understanding the security of modern image captchas and underground captcha-solving services. Big Data Mining Anal. 2(2), 118–144 (2019)
Wu, X., Xu, K., Hall, P.: A survey of image synthesis and editing with generative adversarial networks. Tsinghua Sci. Technol. 22(6), 660–674 (2017)
Deng, H., Zhang, Y., Li, R., Hu, C., Feng, Z., Li, H.: Combining residual attention mechanisms and generative adversarial networks for hippocampus segmentation. Tsinghua Sci. Technol. 27(1), 68–78 (2022)
Rusu, R.B., Marton, Z.C., Blodow, N., et al.: Learning informative point classes for the acquisition of object model maps. In: 2008 10th International Conference on Control, Automation, Robotics and Vision, pp. 643–650. IEEE (2008)
Rusu, R.B., Blodow, N., Marton, Z.C., et al.: Aligning point cloud views using persistent feature histograms. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3384–3391. IEEE (2008)
Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (FPFH) for 3D registration. In: 2009 IEEE International Conference on Robotics and Automation, pp. 3212–3217. IEEE (2009)
Rusu, R.B., Bradski, G., Thibaux, R., et al.: Fast 3D recognition and pose using the viewpoint feature histogram. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2155–2162. IEEE (2010)
Stein, F., Medioni, G.: Structural indexing: efficient 3-D object recognition. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 125–145 (1992)
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: International Conference on Learning Representations, California (2015)
Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: DeepFool: a simple and accurate method to fool deep neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition, Nevada, pp. 2574–2582. IEEE (2016)
Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: IEEE Conference on Computer Vision and Pattern Recognition, Hawaii, pp. 86–94. IEEE (2017)
Xiang, C., Qi, C.R., Li, B.: Generating 3D adversarial point clouds. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9128–9136 (2019). https://doi.org/10.1109/CVPR.2019.00935
Acknowledgments
This work was partially supported by the Gansu Provincial Science and Technology Major Special Innovation Consortium Project (No. 21ZD3GA002), the name of the innovation consortium is Gansu Province Green and Smart Highway Transportation Innovation Consortium, and the project name is Gansu Province Green and Smart Highway Key Technology Research and Demonstration.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhou, Q., Lei, M., Zhi, P., Zhao, R., Shen, J., Yong, B. (2023). Towards Improving the Anti-attack Capability of the RangeNet++. In: Zheng, Y., Keleş, H.Y., Koniusz, P. (eds) Computer Vision – ACCV 2022 Workshops. ACCV 2022. Lecture Notes in Computer Science, vol 13848. Springer, Cham. https://doi.org/10.1007/978-3-031-27066-6_5
Download citation
DOI: https://doi.org/10.1007/978-3-031-27066-6_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-27065-9
Online ISBN: 978-3-031-27066-6
eBook Packages: Computer ScienceComputer Science (R0)