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Verifying robustness of neural networks given a specified threat model is a fundamental yet challenging task.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Manitest: Are classifiers really invariant?
Alhussein Fawzi and Pascal Frossard · 2015
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A rotation and a translation suffice: Fooling cnns with simple transformations
Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, and Aleksander Madry · 2017
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Reluplex: An efficient smt solver for verifying deep neural networks
Guy Katz, Clark Barrett, David L Dill, Kyle Julian, and Mykel J Kochenderfer · 2017
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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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Semantic adversarial examples
Hossein Hosseini and Radha Poovendran · 2018
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Provable defenses against adversarial examples via the convex outer adversarial polytope
J Zico Kolter and Eric Wong · 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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Semidefinite relaxations for certifying robustness to adversarial examples
Aditi Raghunathan, Jacob Steinhardt, and Percy S 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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Boosting robustness certification of neural networks
Gagandeep Singh, Timon Gehr, Markus Püschel, and Martin Vechev · 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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Towards fast computation of certified robustness for relu networks
Tsui-Wei Weng, Huan Zhang, Hongge Chen, Zhao Song, Cho-Jui Hsieh, Duane Boning, Inderjit S Dhillon, and Luca Daniel · 2018
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Evaluating the robustness of neural networks: An extreme value theory approach
Certifying geometric robustness of neural networks
Mislav Balunovic, Maximilian Baader, Gagandeep Singh, Timon Gehr, and Martin Vechev · 2019
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Big but imperceptible adversarial perturbations via semantic manipulation
Anand Bhattad, Min Jin Chong, Kaizhao Liang, Bo Li, and David A Forsyth · 2019
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Cnn-cert: An efficient framework for certifying robustness of convolutional neural networks
Akhilan Boopathy, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, and Luca Daniel · 2019
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Towards analyzing semantic robustness of deep neural networks
Abdullah Hamdi and Bernard Ghanem · 2019
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Semantic adversarial attacks: Parametric transformations that fool deep classifiers
Ameya Joshi, Amitangshu Mukherjee, Soumik Sarkar, and Chinmay Hegde · 2019
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Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Gao, Cho-Jui Hsieh, and Luca Daniel · 2018
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Scaling provable adversarial defenses
Eric Wong, Frank Schmidt, Jan Hendrik Metzen, and J Zico Kolter · 2018
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Efficient neural network robustness certification with general activation functions
Huan Zhang, Tsui-Wei Weng, Pin-Yu Chen, Cho-Jui Hsieh, and Luca Daniel · 2018
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Beyond pixel norm-balls: Parametric adversaries using an analytically differentiable renderer
Hsueh-Ti Derek Liu, Michael Tao, Chun-Liang Li, Derek Nowrouzezahrai, and Alec Jacobson · 2019
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An abstract domain for certifying neural networks
Gagandeep Singh, Timon Gehr, Markus Püschel, and Martin Vechev · 2019
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