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Randomized smoothing has achieved state-of-the-art certified robustness against $l_2$-norm adversarial attacks.
Neural network ensembles
L.K. Hansen and P. Salamon · 1990
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Neural network ensembles, cross validation, and active learning
Anders Krogh and Jesper Vedelsby · 1994
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Rademacher and gaussian complexities: Risk bounds and structural results
Peter Bartlett and Shahar Mendelson · 2001
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Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning
Ali Rahimi and Benjamin Recht · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Šrndić, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
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Very deep convolutional networks for large-scale image recognition
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Mitigating evasion attacks to deep neural networks via region-based classification
Xiaoyu Cao and Neil Zhenqiang Gong · 2017
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Adversarial examples are not easily detected: Bypassing ten detection methods
Nicholas Carlini and David Wagner · 2017
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Formal verification of piece-wise linear feed-forward neural networks
Rüdiger Ehlers · 2017
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Deep neural networks as 0-1 mixed integer linear programs: A feasibility study
Matteo Fischetti and Jason Jo · 2017
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Reluplex: An efficient smt solver for verifying deep neural networks
Guy Katz, Clark W. Barrett, David L. Dill, Kyle Julian, and Mykel J. Kochenderfer · 2017
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2017
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Adversarial machine learning at scale
Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio · 2017
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An approach to reachability analysis for feed-forward relu neural networks
Alessio Lomuscio and Lalit Maganti · 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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A dual approach to scalable verification of deep networks
Krishnamurthy Dvijotham, Robert Stanforth, Sven Gowal, Timothy Mann, and Pushmeet Kohli · 2018
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Ai2: Safety and robustness certification of neural networks with abstract interpretation
Timon Gehr, Matthew Mirman, Dana Drachsler-Cohen, Petar Tsankov, Swarat Chaudhuri, and Martin Vechev · 2018
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On the effectiveness of interval bound propagation for training verifiably robust models
Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Relja Arandjelovic, Timothy A. Mann, and Pushmeet Kohli · 2018
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Second-order adversarial attack and certifiable robustness
Bai Li, Changyou Chen, Wenlin Wang, and Lawrence Carin · 2018
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Towards robust neural networks via random self-ensemble
Xuanqing Liu, Minhao Cheng, Huan Zhang, and Cho-Jui Hsieh · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Adaptive ensemble prediction for deep neural networks based on confidence level
Hiroshi Inoue · 2019
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Certified robustness to adversarial examples with differential privacy
Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, and Suman Jana · 2019
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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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Improving adversarial robustness via promoting ensemble diversity
Tianyu Pang, Kun Xu, Chao Du, Ning Chen, and Jun Zhu · 2019
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Provably robust deep learning via adversarially trained smoothed classifiers
Hadi Salman, Jerry Li, Ilya Razenshteyn, Pengchuan Zhang, Huan Zhang, Sebastien Bubeck, and Greg Yang · 2019
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A convex relaxation barrier to tight robustness verification of neural networks
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Differentiable abstract interpretation for provably robust neural networks
Matthew Mirman, Timon Gehr, and Martin Vechev · 2018
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Certified defenses against adversarial examples
Aditi Raghunathan, Jacob Steinhardt, and Percy Liang · 2018
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Semidefinite relaxations for certifying robustness to adversarial examples
Aditi Raghunathan, Jacob Steinhardt, and Percy Liang · 2018
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Fast and effective robustness certification
Gagandeep Singh, Timon Gehr, Matthew Mirman, Markus Püschel, and Martin Vechev · 2018
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Ensemble methods as a defense to adversarial perturbations against deep neural networks
Thilo Strauss, Markus Hanselmann, Andrej Junginger, and Holger Ulmer · 2018
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Adversarial risk and the dangers of evaluating against weak attacks
Jonathan Uesato, Brendan O’Donoghue, Pushmeet Kohli, and Aäron van den Oord · 2018
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Efficient formal safety analysis of neural networks
Shiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang, and Suman Jana · 2018
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Hadi Salman, Greg Yang, Huan Zhang, Cho-Jui Hsieh, and Pengchuan Zhang · 2019
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ℓ 1 \ell_{1} adversarial robustness certificates: a randomized smoothing approach
Jiaye Teng, Guang-He Lee, and Yang Yuan · 2019
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Evaluating robustness of neural networks with mixed integer programming
Vincent Tjeng, Kai Xiao, and Russ Tedrake · 2019
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Resnets ensemble via the feynman-kac formalism to improve natural and robust accuracies
Bao Wang, Zuoqiang Shi, and Stanley Osher · 2019
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Random smoothing might be unable to certify ℓ ∞ \ell_{\infty} robustness for high-dimensional images
Avrim Blum, Travis Dick, Naren Manoj, and Hongyang Zhang · 2020
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Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Francesco Croce and Matthias Hein · 2020
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Curse of dimensionality on randomized smoothing for certifiable robustness
Aounon Kumar, Alexander Levine, Tom Goldstein, and Soheil Feizi · 2020
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Ying Meng, Jianhai Su, Jason O’Kane, and Pooyan Jamshidi · 2020
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Empir: Ensembles of mixed precision deep networks for increased robustness against adversarial attacks
Sanchari Sen, Balaraman Ravindran, and Anand Raghunathan · 2020
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On adaptive attacks to adversarial example defenses
Florian Tramer, Nicholas Carlini, Wieland Brendel, and Aleksander Madry · 2020
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Randomized smoothing of all shapes and sizes
Greg Yang, Tony Duan, Edward Hu, Hadi Salman, Ilya P. Razenshteyn, and Jerry Li · 2020
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Macer: Attack-free and scalable robust training via maximizing certified radius
Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, and Liwei Wang · 2020
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Black-box certification with randomized smoothing: A functional optimization based framework
Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, and Qiang Liu · 2020
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