Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications

24 August 2021

Papers citing "Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications"

10 / 10 papers shown

Title
Towards Verifying the Geometric Robustness of Large-scale Neural Networks Fu Lee Wang Peipei Xu Wenjie Ruan Xiaowei Huang AAML 50 10 0 29 Jan 2023
Reachability Analysis of Neural Network Control Systems Chi Zhang W. Ruan Peipei Xu 29 13 0 28 Jan 2023
AccelAT: A Framework for Accelerating the Adversarial Training of Deep Neural Networks through Accuracy Gradient F. Nikfam Alberto Marchisio Maurizio Martina Muhammad Shafique AAML 26 0 0 13 Oct 2022
Sparse Adversarial Video Attacks with Spatial Transformations Ronghui Mu Wenjie Ruan Leandro Soriano Marcolino Q. Ni AAML 30 18 0 10 Nov 2021
Fooling Object Detectors: Adversarial Attacks by Half-Neighbor Masks Yanghao Zhang Fu Lee Wang Wenjie Ruan AAML 53 10 0 04 Jan 2021
A Safety Framework for Critical Systems Utilising Deep Neural Networks Xingyu Zhao Alec Banks James Sharp Valentin Robu David Flynn Michael Fisher Xiaowei Huang AAML 50 48 0 07 Mar 2020
Output Reachable Set Estimation and Verification for Multi-Layer Neural Networks Weiming Xiang Hoang-Dung Tran Taylor T. Johnson 88 293 0 09 Aug 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 249 1,842 0 03 Feb 2017
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 296 3,112 0 04 Nov 2016
Safety Verification of Deep Neural Networks Xiaowei Huang Marta Kwiatkowska Sen Wang Min Wu AAML 180 932 0 21 Oct 2016