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We study the effect of adversarial perturbations of images on the estimates of disparity by deep learning models trained for stereo.
AANet: Adaptive Aggregation Network for Efficient Stereo Matching
Xu, H.; and Zhang, J. 2020 · 1968
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Determining optical flow
Horn, B. K.; and Schunck, B. G. 1981 · 1981
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Prosopagnosia: anatomic basis and behavioral mechanisms
Damasio, A. R.; Damasio, H.; and Van Hoesen, G. W. 1982 · 1982
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Object recognition from local scale-invariant features
Lowe, D. G. 1999 · 1999
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Targeted Adversarial Perturbations for Monocular Depth Prediction
Wong, A.; Cicek, S.; and Soatto, S. 2020 · 2006
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Are we ready for autonomous driving? the kitti vision benchmark suite
Geiger, A.; Lenz, P.; and Urtasun, R. 2012 · 2012
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An invitation to 3-d vision: from images to geometric models , volume 26
Ma, Y.; Soatto, S.; Kosecka, J.; and Sastry, S. S. 2012 · 2012
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Intriguing properties of neural networks
Szegedy, C.; Zaremba, W.; Sutskever, I.; Bruna, J.; Erhan, D.; Goodfellow, I.; and Fergus, R. 2013 · 2013
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Explaining and harnessing adversarial examples
Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2014 · 2014
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Object scene flow for autonomous vehicles
Menze, M.; and Geiger, A. 2015 · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A.; Yosinski, J.; and Clune, J. 2015 · 2015
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Learning to compare image patches via convolutional neural networks
Zagoruyko, S.; and Komodakis, N. 2015 · 2015
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Adversarial machine learning at scale
Kurakin, A.; Goodfellow, I.; and Bengio, S. 2016 · 2016
Cited alongside, same era.
A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
Mayer, N.; Ilg, E.; Hausser, P.; Fischer, P.; Cremers, D.; Dosovitskiy, A.; and Brox, T. 2016 · 2016
Cited alongside, same era.
Deepfool: a simple and accurate method to fool deep neural networks
Moosavi-Dezfooli, S.-M.; Fawzi, A.; and Frossard, P. 2016 · 2016
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Stereo matching by training a convolutional neural network to compare image patches
Žbontar, J.; and LeCun, Y. 2016 · 2016
Cited alongside, same era.
Ensemble adversarial training: Attacks and defenses
Tramèr, F.; Kurakin, A.; Papernot, N.; Goodfellow, I.; Boneh, D.; and McDaniel, P. 2017 · 2017
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Pyramid stereo matching network
Chang, J.-R.; and Chen, Y.-S. 2018 · 2018
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Boosting adversarial attacks with momentum
Dong, Y.; Liao, F.; Pang, T.; Su, H.; Zhu, J.; Hu, X.; and Li, J. 2018 · 2018
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Generalizable data-free objective for crafting universal adversarial perturbations
Mopuri, K. R.; Ganeshan, A.; and Babu, R. V. 2018 · 2018
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How do neural networks see depth in single images?
Dijk, T. v.; and Croon, G. d. 2019 · 2019
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Deeppruner: Learning efficient stereo matching via differentiable patchmatch
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Universal adversarial perturbations against semantic image segmentation
Hendrik Metzen, J.; Chaithanya Kumar, M.; Brox, T.; and Fischer, V. 2017 · 2017
Cited alongside, same era.
End-to-end learning of geometry and context for deep stereo regression
Kendall, A.; Martirosyan, H.; Dasgupta, S.; Henry, P.; Kennedy, R.; Bachrach, A.; and Bry, A. 2017 · 2017
Cited alongside, same era.
Universal adversarial perturbations
Moosavi-Dezfooli, S.-M.; Fawzi, A.; Fawzi, O.; and Frossard, P. 2017 · 2017
Cited alongside, same era.
Cascade residual learning: A two-stage convolutional neural network for stereo matching
Pang, J.; Sun, W.; Ren, J. S.; Yang, C.; and Yan, Q. 2017 · 2017
Cited alongside, same era.
Lower bounds on the robustness to adversarial perturbations
Peck, J.; Roels, J.; Goossens, B.; and Saeys, Y. 2017 · 2017
Cited alongside, same era.
Mitigating adversarial effects through randomization
Xie, C.; Wang, J.; Zhang, Z.; Ren, Z.; and Yuille, A. 2017a
Cited in the paper.
Adversarial examples for semantic segmentation and object detection
Xie, C.; Wang, J.; Zhang, Z.; Zhou, Y.; Xie, L.; and Yuille, A. 2017b
Cited in the paper.
Duggal, S.; Wang, S.; Ma, W.-C.; Hu, R.; and Urtasun, R. 2019 · 2019
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Adversarial examples are not bugs, they are features
Ilyas, A.; Santurkar, S.; Tsipras, D.; Engstrom, L.; Tran, B.; and Madry, A. 2019 · 2019
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Cross-domain transferability of adversarial perturbations
Naseer, M. M.; Khan, S. H.; Khan, M. H.; Khan, F. S.; and Porikli, F. 2019 · 2019
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Attacking optical flow
Ranjan, A.; Janai, J.; Geiger, A.; and Black, M. J. 2019 · 2019
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Improving transferability of adversarial examples with input diversity
Xie, C.; Zhang, Z.; Zhou, Y.; Bai, S.; Wang, J.; Ren, Z.; and Yuille, A. L. 2019 · 2019
Later among the works it cites.