Efficient and Effective Augmentation Strategy for Adversarial Training

27 October 2022

Papers citing "Efficient and Effective Augmentation Strategy for Adversarial Training"

6 / 6 papers shown

Title
STBA: Towards Evaluating the Robustness of DNNs for Query-Limited Black-box Scenario Renyang Liu Kwok-Yan Lam Wei Zhou Sixing Wu Jun Zhao Dongting Hu Mingming Gong AAML 28 0 0 30 Mar 2024
PubDef: Defending Against Transfer Attacks From Public Models Chawin Sitawarin Jaewon Chang David Huang Wesson Altoyan David A. Wagner AAML 31 5 0 26 Oct 2023
Decoupled Kullback-Leibler Divergence Loss Jiequan Cui Zhuotao Tian Zhisheng Zhong Xiaojuan Qi Bei Yu Hanwang Zhang 39 38 0 23 May 2023
DART: Diversify-Aggregate-Repeat Training Improves Generalization of Neural Networks Samyak Jain Sravanti Addepalli P. Sahu Priyam Dey R. Venkatesh Babu MoMe OOD 43 20 0 28 Feb 2023
Understanding the Vulnerability of Skeleton-based Human Activity Recognition via Black-box Attack Yunfeng Diao He-Nan Wang Tianjia Shao Yong-Liang Yang Kun Zhou David C. Hogg Meng Wang AAML 40 6 0 21 Nov 2022
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 231 677 0 19 Oct 2020