Fetching the paper…
Reading the bibliography…
Deep neural networks are known to be vulnerable to adversarial examples which are carefully crafted instances to cause the models to make wrong predictions.
A density-based algorithm for discovering clusters in large spatial databases with noise
M. Ester, H.-P. Kriegel, J. Sander, X. Xu, et al · 1996
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
I. Goodfellow, J. Shlens, and C. Szegedy · 2014
Earlier work this paper cites.
3d shapenets: A deep representation for volumetric shapes
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao · 2015
Earlier work this paper cites.
Defensive distillation is not robust to adversarial examples
N. Carlini and D. Wagner · 2016
Earlier work this paper cites.
Adversarial examples in the physical world
A. Kurakin, I. Goodfellow, and S. Bengio · 2016
Earlier work this paper cites.
Deepfool: a simple and accurate method to fool deep neural networks
S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 2016
Earlier work this paper cites.
The limitations of deep learning in adversarial settings
N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami · 2016
Earlier work this paper cites.
Pointnet: Deep learning on point sets for 3d classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2016
Earlier work this paper cites.
Synthesizing robust adversarial examples
A. Athalye and I. Sutskever · 2017
Earlier work this paper cites.
Towards evaluating the robustness of neural networks
N. Carlini and D. Wagner · 2017
Earlier work this paper cites.
Robust physical-world attacks on deep learning models
I. Evtimov, K. Eykholt, E. Fernandes, T. Kohno, B. Li, A. Prakash, A. Rahmati, and D. Song · 2017
Cited alongside, same era.
A point set generation network for 3d object reconstruction from a single image
H. Fan, H. Su, and L. J. Guibas · 2017
Cited alongside, same era.
Adversarial examples for evaluating reading comprehension systems
R. Jia and P. Liang · 2017
Cited alongside, same era.
Large-scale point cloud semantic segmentation with superpoint graphs
L. Landrieu and M. Simonovsky · 2017
Cited alongside, same era.
Magnet: a two-pronged defense against adversarial examples
D. Meng and H. Chen · 2017
Cited alongside, same era.
Audio adversarial examples: Targeted attacks on speech-to-text
N. Carlini and D. Wagner · 2018
Closest in time.
Adagio: Interactive experimentation with adversarial attack and defense for audio
N. Das, M. Shanbhogue, S.-T. Chen, L. Chen, M. E. Kounavis, and D. H. Chau · 2018
Closest in time.
Frustum pointnets for 3d object detection from rgb-d data
C. R. Qi, W. Liu, C. Wu, H. Su, and L. J. Guibas · 2018
Closest in time.
Defense-gan: Protecting classifiers against adversarial attacks using generative models
P. Samangouei, M. Kabkab, and R. Chellappa · 2018
Closest in time.
A deeper look at 3d shape classifiers
J.-C. Su, M. Gadelha, R. Wang, and S. Maji · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Practical black-box attacks against machine learning
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami · 2017
Cited alongside, same era.
Pointnet++: Deep hierarchical feature learning on point sets in a metric space
C. R. Qi, L. Yi, H. Su, and L. J. Guibas · 2017
Cited alongside, same era.
Improving network robustness against adversarial attacks with compact convolution
R. Ranjan, S. Sankaranarayanan, C. D. Castillo, and R. Chellappa · 2017
Cited alongside, same era.
Orientation-boosted voxel nets for 3d object recognition
N. Sedaghat, M. Zolfaghari, E. Amiri, and T. Brox · 2017
Cited alongside, same era.
Complementme: weakly-supervised component suggestions for 3d modeling
M. Sung, H. Su, V. G. Kim, S. Chaudhuri, and L. Guibas · 2017
Cited alongside, same era.
Feature squeezing: Detecting adversarial examples in deep neural networks
W. Xu, D. Evans, and Y. Qi · 2017
Cited alongside, same era.
Generating natural adversarial examples
Z. Zhao, D. Dua, and S. Singh · 2017
Cited alongside, same era.
Closest in time.
Dynamic graph cnn for learning on point clouds
Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon · 2018
Closest in time.
Generating adversarial examples with adversarial networks
C. Xiao, B. Li, J.-Y. Zhu, W. He, M. Liu, and D. Song · 2018
Closest in time.
Spatially transformed adversarial examples
C. Xiao, J.-Y. Zhu, B. Li, W. He, M. Liu, and D. Song · 2018
Closest in time.
Pointfusion: Deep sensor fusion for 3d bounding box estimation
D. Xu, D. Anguelov, and A. Jain · 2018
Closest in time.
Deepdefense: Training deep neural networks with improved robustness
Z. Yan, Y. Guo, and C. Zhang · 2018
Closest in time.
Voxelnet: End-to-end learning for point cloud based 3d object detection
Y. Zhou and O. Tuzel · 2018
Closest in time.