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The study of provable adversarial robustness has mostly been limited to classification tasks and models with one-dimensional real-valued outputs.
On instabilities of deep learning in image reconstruction - does AI come at a cost?
Vegard Antun, Francesco Renna, Clarice Poon, Ben Adcock, and Anders C. Hansen · 1902
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Non-local context encoder: Robust biomedical image segmentation against adversarial attacks
Xiang He, Sibei Yang, Guanbin Li, Haofeng Li, Huiyou Chang, and Yizhou Yu · 1904
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Approximate clustering via core-sets
Mihai Bundefineddoiu, Sariel Har-Peled, and Piotr Indyk · 2002
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(de)randomized smoothing for certifiable defense against patch attacks
Alexander Levine and Soheil Feizi · 2002
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The mnist database of handwritten digit images for machine learning research [best of the web]
Li Deng · 2012
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus · 2014
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Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Complexity and approximation of the smallest k-enclosing ball problem
Vladimir Shenmaier · 2015
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Joint face detection and alignment using multi-task cascaded convolutional networks
Kaipeng Zhang, Zhanpeng Zhang, Zhifeng Li, and Yu Qiao · 2016
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On the robustness of semantic segmentation models to adversarial attacks
Anurag Arnab, Ondrej Miksik, and Philip H. S. Torr · 2017
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Vulnerability of deep reinforcement learning to policy induction attacks
Vahid Behzadan and Arslan Munir · 2017
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Adversarial examples are not easily detected: Bypassing ten detection methods
Nicholas Carlini and David A. Wagner · 2017
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Show-and-fool: Crafting adversarial examples for neural image captioning
Hongge Chen, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, and Cho-Jui Hsieh · 2017
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Adversarial and clean data are not twins
Zhitao Gong, Wenlu Wang, and Wei-Shinn Ku · 2017
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On the (statistical) detection of adversarial examples
Kathrin Grosse, Praveen Manoharan, Nicolas Papernot, Michael Backes, and Patrick D. McDaniel · 2017
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Adversarial attacks on neural network policies
Sandy H. Huang, Nicolas Papernot, Ian J. Goodfellow, Yan Duan, and Pieter Abbeel · 2017
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Adversarial machine learning at scale
Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio · 2017
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Adversarial examples detection in deep networks with convolutional filter statistics
Xin Li and Fuxin Li · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
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Thermometer encoding: One hot way to resist adversarial examples
Jacob Buckman, Aurko Roy, Colin Raffel, and Ian J. Goodfellow · 2018
Earlier work this paper cites.
Audio adversarial examples: Targeted attacks on speech-to-text
Nicholas Carlini and David A. Wagner · 2018
Cited alongside, same era.
Stochastic activation pruning for robust adversarial defense
Guneet S. Dhillon, Kamyar Azizzadenesheli, Zachary C. Lipton, Jeremy Bernstein, Jean Kossaifi, Aran Khanna, and Animashree Anandkumar · 2018
Cited alongside, same era.
Training verified learners with learned verifiers, 2018
Krishnamurthy Dvijotham, Sven Gowal, Robert Stanforth, Relja Arandjelovic, Brendan O’Donoghue, Jonathan Uesato, and Pushmeet Kohli · 2018
Cited alongside, same era.
On the effectiveness of interval bound propagation for training verifiably robust models, 2018
Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Relja Arandjelovic, Timothy Mann, and Pushmeet Kohli · 2018
Cited alongside, same era.
Countering adversarial images using input transformations
Chuan Guo, Mayank Rana, Moustapha Cissé, and Laurens van der Maaten · 2018
Cited alongside, same era.
Tight certificates of adversarial robustness for randomly smoothed classifiers
Guang-He Lee, Yang Yuan, Shiyu Chang, and Tommi S. Jaakkola · 2019
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Wasserstein smoothing: Certified robustness against wasserstein adversarial attacks, 2019
Alexander Levine and Soheil Feizi · 2019
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Certified adversarial robustness with additive noise
Bai Li, Changyou Chen, Wenlin Wang, and Lawrence Carin · 2019
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Provably robust deep learning via adversarially trained smoothed classifiers
Hadi Salman, Jerry Li, Ilya P. Razenshteyn, Pengchuan Zhang, Huan Zhang, Sébastien Bubeck, and Greg Yang · 2019
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Robustness certificates against adversarial examples for relu networks
Sahil Singla and Soheil Feizi · 2019
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Adversarial examples for generative models
Jernej Kos, Ian Fischer, and Dawn Song · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Cited alongside, same era.
Differentiable abstract interpretation for provably robust neural networks
Matthew Mirman, Timon Gehr, and Martin Vechev · 2018
Cited alongside, same era.
Robust deep reinforcement learning with adversarial attacks
Anay Pattanaik, Zhenyi Tang, Shuijing Liu, Gautham Bommannan, and Girish Chowdhary · 2018
Cited alongside, same era.
Semidefinite relaxations for certifying robustness to adversarial examples
Aditi Raghunathan, Jacob Steinhardt, and Percy Liang · 2018
Cited alongside, same era.
Attacks on state-of-the-art face recognition using attentional adversarial attack generative network
Qing Song, Yingqi Wu, and Lu Yang · 2018
Cited alongside, same era.
Adversarial risk and the dangers of evaluating against weak attacks
Jonathan Uesato, Brendan O’Donoghue, Pushmeet Kohli, and Aäron van den Oord · 2018
Cited alongside, same era.
Making medical image reconstruction adversarially robust
Adva Wolf · 2019
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Adversarial robust training in mri reconstruction
Francesco Calivá, Kaiyang Cheng, Rutwik Shah, and Valentina Pedoia · 2020
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Addressing the false negative problem of deep learning mri reconstruction models by adversarial attacks and robust training
Kaiyang Cheng, Francesco Calivá, Rutwik Shah, Misung Han, Sharmila Majumdar, and Valentina Pedoia · 2020
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Certified defenses for adversarial patches
Ping-yeh Chiang, Renkun Ni, Ahmed Abdelkader, Chen Zhu, Christoph Studer, and Tom Goldstein · 2020
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Adversarial attack on facial recognition using visible light
Morgan Frearson and Kien Nguyen · 2020
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Adversarial policies: Attacking deep reinforcement learning
Adam Gleave, Michael Dennis, Cody Wild, Neel Kant, Sergey Levine, and Stuart Russell · 2020
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Adversarial attacks for image segmentation on multiple lightweight models
Xu Kang, Bin Song, Xiaojiang Du, and Mohsen Guizani · 2020
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Certifying confidence via randomized smoothing
Aounon Kumar, Alexander Levine, Soheil Feizi, and Tom Goldstein · 2020
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Robustness certificates for sparse adversarial attacks by randomized ablation
Alexander Levine and Soheil Feizi · 2020
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Tight second-order certificates for randomized smoothing, 2020
Alexander Levine, Aounon Kumar, Thomas Goldstein, and Soheil Feizi · 2020
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Improving robustness of deep-learning-based image reconstruction
Ankit Raj, Yoram Bresler, and Bo Li · 2020
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Second-order provable defenses against adversarial attacks, 2020
Sahil Singla and Soheil Feizi · 2020
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ℓ 1 \ell_{1} adversarial robustness certificates: a randomized smoothing approach, 2020
Jiaye Teng, Guang-He Lee, and Yang Yuan · 2020
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Threat of adversarial attacks on face recognition: A comprehensive survey
Fatemeh Vakhshiteh, Raghavendra Ramachandra, and Ahmad Nickabadi · 2020
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Detection as regression: Certified object detection by median smoothing, 2020
Ping yeh Chiang, Michael J. Curry, Ahmed Abdelkader, Aounon Kumar, John Dickerson, and Tom Goldstein · 2020
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Scalable certified segmentation via randomized smoothing
Marc Fischer, Maximilian Baader, and Martin T. Vechev · 2021
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