Built on

Lee, D.T., Schachter, B.J.: Two algorithms for constructing a Delaunay triangulation. International Journal of Computer & Information Sciences 9
1980
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Edelsbrunner, H., Kirkpatrick, D., Seidel, R.: On the shape of a set of points in the plane. IEEE Transactions on Information Theory 29
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Yianilos, P.N.: Data Structures and Algorithms for Nearest Neighbor Search in General Metric Spaces. In: Proceedings of the fourth annual ACM-SIAM symposium on Discrete algorithms. vol. 93, pp. 311–21 (1993)
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Bernardini, F., Mittleman, J., Rushmeier, H., Silva, C., Taubin, G.: The ball-pivoting algorithm for surface reconstruction. IEEE Transactions on Visualization and Computer Graphics 5
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Biggio, B., Corona, I., Maiorca, D., Nelson, B., Šrndić, N., Laskov, P., Giacinto, G., Roli, F.: Evasion Attacks Against Machine Learning at Test Time. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. pp. 387–402. Springer (2013)
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Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)
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2013
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Goodfellow, I., Shlens, J., Szegedy, C.: Explaining and Harnessing Adversarial Examples. arXiv preprint arXiv:1412.6572 (2014)
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2014
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Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
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2014
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Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial Examples in the Physical World. arXiv preprint arXiv:1607.02533 (2016)
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2016
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Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial Machine Learning at Scale. arXiv preprint arXiv:1611.01236 (2016)
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2016
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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. pp. 2574–2582 (2016)
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Earlier work this paper cites.

Similar

Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z.B., Swami, A.: The Limitations of Deep Learning in Adversarial Settings. In: 2016 IEEE European Symposium on Security and Privacy (EuroS&P). pp. 372–387. IEEE (2016)
2016
Cited alongside, same era.
Papernot, N., McDaniel, P., Wu, X., Jha, S., Swami, A.: Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks. In: 2016 IEEE Symposium on Security and Privacy (SP). pp. 582–597. IEEE (2016)
2016
Cited alongside, same era.
Brown, T.B., Mané, D., Roy, A., Abadi, M., Gilmer, J.: Adversarial Patch. arXiv preprint arXiv:1712.09665 (2017)
Original
2017
Cited alongside, same era.
Carlini, N., Wagner, D.: Towards Evaluating the Robustness of Neural Networks. In: 2017 IEEE Symposium on Security and Privacy. pp. 39–57. IEEE (2017)
2017
Cited alongside, same era.

Then

Deng, H., Birdal, T., Ilic, S.: PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors. arXiv preprint arXiv:1808.10322 (2018)
Original
2018
Later among the works it cites.
Dong, Y., Liao, F., Pang, T., Su, H., Zhu, J., Hu, X., Li, J.: Boosting Adversarial Attacks with Momentum. arXiv preprint (2018)
2018
Later among the works it cites.
Guo, C., Frank, J.S., Weinberger, K.Q.: Low frequency adversarial perturbation. arXiv preprint arXiv:1809.08758 (2018)
Original
2018
Later among the works it cites.
Xiang, C., Qi, C.R., Li, B.: Generating 3D Adversarial Point Clouds. arXiv preprint arXiv:1809.07016 (2018)
Original
2018
Later among the works it cites.
Zheng, T., Chen, C., Ren, K., et al.: Learning Saliency Maps for Adversarial Point-Cloud Generation. arXiv preprint arXiv:1812.01687 (2018)

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2019

Adversarial shape perturbations on 3D point clouds

Liu, Daniel, Yu, Ronald, Su, Hao

Understand

The importance of training robust neural network grows as 3D data is increasingly utilized in deep learning for vision tasks in robotics, drone control, and autonomous driving.

One commonly used 3D data type is 3D point clouds, which describe shape information.
We examine the problem of creating robust models from the perspective of the attacker, which is necessary in understanding how 3D neural networks can be exploited.
We explore two categories of attacks: distributional attacks that involve imperceptible perturbations to the distribution of points, and shape attacks that involve deforming the shape represented by a point cloud.

Built on

Lee, D.T., Schachter, B.J.: Two algorithms for constructing a Delaunay triangulation. International Journal of Computer & Information Sciences 9
1980
Earlier work this paper cites.
Edelsbrunner, H., Kirkpatrick, D., Seidel, R.: On the shape of a set of points in the plane. IEEE Transactions on Information Theory 29
1983
Earlier work this paper cites.
Yianilos, P.N.: Data Structures and Algorithms for Nearest Neighbor Search in General Metric Spaces. In: Proceedings of the fourth annual ACM-SIAM symposium on Discrete algorithms. vol. 93, pp. 311–21 (1993)
1993
Earlier work this paper cites.
Bernardini, F., Mittleman, J., Rushmeier, H., Silva, C., Taubin, G.: The ball-pivoting algorithm for surface reconstruction. IEEE Transactions on Visualization and Computer Graphics 5
1999
Earlier work this paper cites.
Biggio, B., Corona, I., Maiorca, D., Nelson, B., Šrndić, N., Laskov, P., Giacinto, G., Roli, F.: Evasion Attacks Against Machine Learning at Test Time. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. pp. 387–402. Springer (2013)
2013
Earlier work this paper cites.
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)
Original
2013
Earlier work this paper cites.
Goodfellow, I., Shlens, J., Szegedy, C.: Explaining and Harnessing Adversarial Examples. arXiv preprint arXiv:1412.6572 (2014)
Original
2014
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Original
2014
Earlier work this paper cites.
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: 3D ShapeNets: A Deep Representation for Volumetric Shapes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1912–1920 (2015)
2015
Earlier work this paper cites.
Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial Examples in the Physical World. arXiv preprint arXiv:1607.02533 (2016)
Original
2016
Earlier work this paper cites.
Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial Machine Learning at Scale. arXiv preprint arXiv:1611.01236 (2016)
Original
2016
Earlier work this paper cites.
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. pp. 2574–2582 (2016)
2016
Earlier work this paper cites.

Similar

Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z.B., Swami, A.: The Limitations of Deep Learning in Adversarial Settings. In: 2016 IEEE European Symposium on Security and Privacy (EuroS&P). pp. 372–387. IEEE (2016)
2016
Cited alongside, same era.
Papernot, N., McDaniel, P., Wu, X., Jha, S., Swami, A.: Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks. In: 2016 IEEE Symposium on Security and Privacy (SP). pp. 582–597. IEEE (2016)
2016
Cited alongside, same era.
Brown, T.B., Mané, D., Roy, A., Abadi, M., Gilmer, J.: Adversarial Patch. arXiv preprint arXiv:1712.09665 (2017)
Original
2017
Cited alongside, same era.
Carlini, N., Wagner, D.: Towards Evaluating the Robustness of Neural Networks. In: 2017 IEEE Symposium on Security and Privacy. pp. 39–57. IEEE (2017)
2017
Cited alongside, same era.

Then

Deng, H., Birdal, T., Ilic, S.: PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors. arXiv preprint arXiv:1808.10322 (2018)
Original
2018
Later among the works it cites.
Dong, Y., Liao, F., Pang, T., Su, H., Zhu, J., Hu, X., Li, J.: Boosting Adversarial Attacks with Momentum. arXiv preprint (2018)
2018
Later among the works it cites.
Guo, C., Frank, J.S., Weinberger, K.Q.: Low frequency adversarial perturbation. arXiv preprint arXiv:1809.08758 (2018)
Original
2018
Later among the works it cites.
Xiang, C., Qi, C.R., Li, B.: Generating 3D Adversarial Point Clouds. arXiv preprint arXiv:1809.07016 (2018)
Original
2018
Later among the works it cites.
Zheng, T., Chen, C., Ren, K., et al.: Learning Saliency Maps for Adversarial Point-Cloud Generation. arXiv preprint arXiv:1812.01687 (2018)

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

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Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards Deep Learning Models Resistant to Adversarial Attacks. arXiv preprint arXiv:1706.06083 (2017)

Original

2017

Cited alongside, same era.

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. pp. 1765–1773 (2017)

2017

Cited alongside, same era.

Qi, C.R., Liu, W., Wu, C., Su, H., Guibas, L.J.: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 (2017)

Original

2017

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Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 1

2017

Cited alongside, same era.

Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. In: Advances in Neural Information Processing Systems. pp. 5099–5108 (2017)

2017

Cited alongside, same era.

Wang, P.S., Liu, Y., Guo, Y.X., Sun, C.Y., Tong, X.: O-CNN: Octree-based convolutional neural networks for 3D shape analysis. ACM Transactions on Graphics 36

2017

Cited alongside, same era.

Wong, E., Kolter, J.Z.: Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope. arXiv preprint arXiv:1711.00851 (2017)

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2017

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Biggio, B., Roli, F.: Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning. Pattern Recognition 84

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Later among the works it cites.

Zhou, H., Chen, K., Zhang, W., Fang, H., Zhou, W., Yu, N.: Deflecting 3D Adversarial Point Clouds Through Outlier-Guided Removal. arXiv preprint arXiv:1812.11017 (2018)

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Cao, Y., Xiao, C., Yang, D., Fang, J., Yang, R., Liu, M., Li, B.: Adversarial Objects Against LiDAR-Based Autonomous Driving Systems. arXiv preprint arXiv:1907.05418 (2019)

Original

2019

Closest in time.

Liu, D., Yu, R., Su, H.: Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers. arXiv preprint arXiv:1901.03006 (2019)

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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. pp. 11767–11775 (2019)

2019

Closest in time.

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Closest in time.

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)

2020

Closest in time.