Towards Verifying Robustness of Neural Networks Against Semantic
Perturbations

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Towards Verifying Robustness of Neural Networks Against Semantic Perturbations

19 December 2019

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Papers citing "Towards Verifying Robustness of Neural Networks Against Semantic Perturbations"

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Title
General Lipschitz: Certified Robustness Against Resolvable Semantic Transformations via Transformation-Dependent Randomized Smoothing Dmitrii Korzh Alireza Azadbakht Maryam Tahmasbi Alireza Javaheri AAML 81 0 0 17 Aug 2023
Toward Certified Robustness Against Real-World Distribution Shifts Haoze Wu Teruhiro Tagomori Alexander Robey Fengjun Yang Nikolai Matni George Pappas Hamed Hassani C. Păsăreanu Clark W. Barrett AAML OOD 115 18 0 08 Jun 2022
Guidance on the Assurance of Machine Learning in Autonomous Systems (AMLAS) Richard Hawkins Colin Paterson Chiara Picardi Yan Jia R. Calinescu Ibrahim Habli 90 84 0 02 Feb 2021
Revisiting Edge Detection in Convolutional Neural Networks Minh Le Subhradeep Kayal FAtt 83 13 0 25 Dec 2020
Robust Deep Reinforcement Learning through Adversarial Loss Tuomas P. Oikarinen Wang Zhang Alexandre Megretski Luca Daniel Tsui-Wei Weng AAML 88 96 0 05 Aug 2020
Towards Robustness against Unsuspicious Adversarial Examples Liang Tong Minzhe Guo A. Prakash Yevgeniy Vorobeychik AAML 14 0 0 08 May 2020
Improved Image Wasserstein Attacks and Defenses J. E. Hu Adith Swaminathan Hadi Salman Greg Yang AAML OOD 88 10 0 26 Apr 2020
Certified Defense to Image Transformations via Randomized Smoothing Marc Fischer Maximilian Baader Martin Vechev AAML 87 67 0 27 Feb 2020