Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable
Example Attacks

Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks

27 March 2023

Papers citing "Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks"

10 / 10 papers shown

Title
MTL-UE: Learning to Learn Nothing for Multi-Task Learning Yi Yu Song Xia Siyuan Yang Chenqi Kong Wenhan Yang Shijian Lu Yap-Peng Tan Alex Chichung Kot 46 0 0 08 May 2025
A Survey on Unlearnable Data Jiahao Li Yiqiang Chen Yunbing Xing Yang Gu Xiangyuan Lan AAML 58 0 0 30 Mar 2025
Nonlinear Transformations Against Unlearnable Datasets T. Hapuarachchi Jing Lin Kaiqi Xiong Mohamed Rahouti Gitte Ost 28 1 0 05 Jun 2024
Effective and Robust Adversarial Training against Data and Label Corruptions Pengfei Zhang Zi Huang Xin-Shun Xu Guangdong Bai 51 4 0 07 May 2024
Purify Unlearnable Examples via Rate-Constrained Variational Autoencoders Yi Yu Yufei Wang Song Xia Wenhan Yang Shijian Lu Yap-Peng Tan A.C. Kot AAML 37 11 0 02 May 2024
Transferable Availability Poisoning Attacks Yiyong Liu Michael Backes Xiao Zhang AAML 19 3 0 08 Oct 2023
Unlearnable Examples Give a False Sense of Security: Piercing through Unexploitable Data with Learnable Examples Wanzhu Jiang Yunfeng Diao He-Nan Wang Jianxin Sun Hao Wu Richang Hong 37 18 0 16 May 2023
Unlearnable Examples: Making Personal Data Unexploitable Hanxun Huang Xingjun Ma S. Erfani James Bailey Yisen Wang MIACV 147 190 0 13 Jan 2021
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 228 677 0 19 Oct 2020
Densely Connected Convolutional Networks Gao Huang Zhuang Liu L. V. D. van der Maaten Kilian Q. Weinberger PINN 3DV 258 36,371 0 25 Aug 2016