Nonlinear Transformations Against Unlearnable Datasets

5 June 2024

Papers citing "Nonlinear Transformations Against Unlearnable Datasets"

24 / 24 papers shown

Title
What Can We Learn from Unlearnable Datasets? Pedro Sandoval-Segura Vasu Singla Jonas Geiping Micah Goldblum Tom Goldstein 60 15 0 30 May 2023
Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks Tianrui Qin Xitong Gao Juanjuan Zhao Kejiang Ye Chengzhong Xu AAML MU 58 27 0 27 Mar 2023
Image Shortcut Squeezing: Countering Perturbative Availability Poisons with Compression Zhuoran Liu Zhengyu Zhao Martha Larson 68 37 0 31 Jan 2023
Self-Ensemble Protection: Training Checkpoints Are Good Data Protectors Sizhe Chen Geng Yuan Xinwen Cheng Yifan Gong Minghai Qin Yanzhi Wang Xiaolin Huang AAML 51 20 0 22 Nov 2022
Generative Poisoning Using Random Discriminators Dirren van Vlijmen A. Kolmus Zhuoran Liu Zhengyu Zhao Martha Larson 43 2 0 02 Nov 2022
Autoregressive Perturbations for Data Poisoning Pedro Sandoval-Segura Vasu Singla Jonas Geiping Micah Goldblum Tom Goldstein David Jacobs AAML 57 41 0 08 Jun 2022
Indiscriminate Poisoning Attacks on Unsupervised Contrastive Learning Hao He Kaiwen Zha Dina Katabi AAML 74 34 0 22 Feb 2022
Can Adversarial Training Be Manipulated By Non-Robust Features? Lue Tao Lei Feng Hongxin Wei Jinfeng Yi Sheng-Jun Huang Songcan Chen AAML 196 17 0 31 Jan 2022
Fooling Adversarial Training with Inducing Noise Zhirui Wang Yifei Wang Yisen Wang 60 14 0 19 Nov 2021
Adversarial Examples Make Strong Poisons Liam H. Fowl Micah Goldblum Ping Yeh-Chiang Jonas Geiping Wojtek Czaja Tom Goldstein SILM 80 135 0 21 Jun 2021
Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training Lue Tao Lei Feng Jinfeng Yi Sheng-Jun Huang Songcan Chen AAML 75 73 0 09 Feb 2021
Unlearnable Examples: Making Personal Data Unexploitable Hanxun Huang Xingjun Ma S. Erfani James Bailey Yisen Wang MIACV 215 193 0 13 Jan 2021
DeepSweep: An Evaluation Framework for Mitigating DNN Backdoor Attacks using Data Augmentation Han Qiu Yi Zeng Shangwei Guo Tianwei Zhang Meikang Qiu B. Thuraisingham AAML 62 191 0 13 Dec 2020
FMix: Enhancing Mixed Sample Data Augmentation Ethan Harris Antonia Marcu Matthew Painter M. Niranjan Adam Prugel-Bennett Jonathon S. Hare AAML 53 26 0 27 Feb 2020
Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder Ji Feng Qi-Zhi Cai Zhi Zhou AAML 54 105 0 22 May 2019
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features Sangdoo Yun Dongyoon Han Seong Joon Oh Sanghyuk Chun Junsuk Choe Y. Yoo OOD 606 4,777 0 13 May 2019
Neural Tangent Kernel: Convergence and Generalization in Neural Networks Arthur Jacot Franck Gabriel Clément Hongler 263 3,194 0 20 Jun 2018
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning Matthew Jagielski Alina Oprea Battista Biggio Chang-rui Liu Cristina Nita-Rotaru Yue Liu AAML 85 759 0 01 Apr 2018
Deep Learning using Rectified Linear Units (ReLU) Abien Fred Agarap 58 3,223 0 22 Mar 2018
A Kernel Theory of Modern Data Augmentation Tri Dao Albert Gu Alexander J. Ratner Virginia Smith Christopher De Sa Christopher Ré 100 193 0 16 Mar 2018
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 299 12,060 0 19 Jun 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 187 2,882 0 14 Mar 2017
Membership Inference Attacks against Machine Learning Models Reza Shokri M. Stronati Congzheng Song Vitaly Shmatikov SLR MIALM MIACV 238 4,120 0 18 Oct 2016
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan Andrew Zisserman FAtt MDE 1.6K 100,330 0 04 Sep 2014