An ADMM-Based Universal Framework for Adversarial Attacks on Deep Neural Networks

9 April 2018

Papers citing "An ADMM-Based Universal Framework for Adversarial Attacks on Deep Neural Networks"

7 / 7 papers shown

Title
Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective Baoyuan Wu Zihao Zhu Li Liu Qingshan Liu Zhaofeng He Siwei Lyu AAML 46 21 0 19 Feb 2023
Transferable Sparse Adversarial Attack Ziwen He Wei Wang Jing Dong Tieniu Tan AAML 19 20 0 31 May 2021
Defending Adversarial Examples via DNN Bottleneck Reinforcement Wenqing Liu Miaojing Shi Teddy Furon Li Li AAML 26 8 0 12 Aug 2020
Towards Understanding the Adversarial Vulnerability of Skeleton-based Action Recognition Tianhang Zheng Sheng Liu Changyou Chen Junsong Yuan Baochun Li K. Ren AAML 21 17 0 14 May 2020
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness Pu Zhao Pin-Yu Chen Payel Das Karthikeyan N. Ramamurthy Xue Lin AAML 58 185 0 30 Apr 2020
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses Tianlin Li Siyue Wang Pin-Yu Chen Yanzhi Wang Brian Kulis Xue Lin S. Chin AAML 16 42 0 20 Aug 2019
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 317 5,847 0 08 Jul 2016