Adversarial Examples Make Strong Poisons

21 June 2021

Papers citing "Adversarial Examples Make Strong Poisons"

46 / 96 papers shown

Title
Unlearnable Examples for Diffusion Models: Protect Data from Unauthorized Exploitation Zhengyue Zhao Jinhao Duan Xingui Hu Kaidi Xu Chenan Wang Rui Zhang Zidong Du Qi Guo Yunji Chen DiffM WIGM 28 27 0 02 Jun 2023
What Can We Learn from Unlearnable Datasets? Pedro Sandoval-Segura Vasu Singla Jonas Geiping Micah Goldblum Tom Goldstein 24 14 0 30 May 2023
Sharpness-Aware Data Poisoning Attack Pengfei He Han Xu J. Ren Yingqian Cui Hui Liu Charu C. Aggarwal Jiliang Tang AAML 44 7 0 24 May 2023
Towards Benchmarking and Assessing Visual Naturalness of Physical World Adversarial Attacks Simin Li Shuing Zhang Gujun Chen Dong Wang Pu Feng Jiakai Wang Aishan Liu Xin Yi Xianglong Liu AAML 9 18 0 22 May 2023
Towards Generalizable Data Protection With Transferable Unlearnable Examples Bin Fang Bo-wen Li Shuang Wu Tianyi Zheng Shouhong Ding Ran Yi Lizhuang Ma 19 4 0 18 May 2023
Re-thinking Data Availablity Attacks Against Deep Neural Networks Bin Fang Bo-wen Li Shuang Wu Ran Yi Shouhong Ding Lizhuang Ma AAML 35 0 0 18 May 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
Pick your Poison: Undetectability versus Robustness in Data Poisoning Attacks Nils Lukas Florian Kerschbaum 36 1 0 07 May 2023
Assessing Vulnerabilities of Adversarial Learning Algorithm through Poisoning Attacks Jingfeng Zhang Bo Song Bo Han Lei Liu Gang Niu Masashi Sugiyama AAML 21 2 0 30 Apr 2023
Probably Approximately Correct Federated Learning Xiaojin Zhang Anbu Huang Lixin Fan Kai Chen Qiang Yang FedML 27 5 0 10 Apr 2023
The Devil's Advocate: Shattering the Illusion of Unexploitable Data using Diffusion Models H. M. Dolatabadi S. Erfani C. Leckie DiffM 54 17 0 15 Mar 2023
CUDA: Convolution-based Unlearnable Datasets Vinu Sankar Sadasivan Mahdi Soltanolkotabi S. Feizi MU 29 25 0 07 Mar 2023
Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks Yiwei Lu Gautam Kamath Yaoliang Yu AAML 39 18 0 07 Mar 2023
Image Shortcut Squeezing: Countering Perturbative Availability Poisons with Compression Zhuoran Liu Zhengyu Zhao Martha Larson 32 34 0 31 Jan 2023
Backdoor Attacks Against Dataset Distillation Yugeng Liu Zheng Li Michael Backes Yun Shen Yang Zhang DD 42 28 0 03 Jan 2023
Unlearnable Clusters: Towards Label-agnostic Unlearnable Examples Jiaming Zhang Xingjun Ma Qiaomin Yi Jitao Sang Yugang Jiang Yaowei Wang Changsheng Xu 21 24 0 31 Dec 2022
Training Data Influence Analysis and Estimation: A Survey Zayd Hammoudeh Daniel Lowd TDI 29 82 0 09 Dec 2022
Pre-trained Encoders in Self-Supervised Learning Improve Secure and Privacy-preserving Supervised Learning Hongbin Liu Wenjie Qu Jinyuan Jia Neil Zhenqiang Gong SSL 28 6 0 06 Dec 2022
Self-Ensemble Protection: Training Checkpoints Are Good Data Protectors Sizhe Chen Geng Yuan Xinwen Cheng Yifan Gong Minghai Qin Yanzhi Wang X. Huang AAML 30 20 0 22 Nov 2022
UPTON: Preventing Authorship Leakage from Public Text Release via Data Poisoning Ziyao Wang Thai Le Dongwon Lee 36 1 0 17 Nov 2022
Differentially Private Optimizers Can Learn Adversarially Robust Models Yuan Zhang Zhiqi Bu 18 3 0 16 Nov 2022
Generative Poisoning Using Random Discriminators Dirren van Vlijmen A. Kolmus Zhuoran Liu Zhengyu Zhao Martha Larson 26 2 0 02 Nov 2022
Transferable Unlearnable Examples J. Ren Han Xu Yuxuan Wan Xingjun Ma Lichao Sun Jiliang Tang 36 36 0 18 Oct 2022
Autoregressive Perturbations for Data Poisoning Pedro Sandoval-Segura Vasu Singla Jonas Geiping Micah Goldblum Tom Goldstein David Jacobs AAML 27 40 0 08 Jun 2022
One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks Shutong Wu Sizhe Chen Cihang Xie X. Huang AAML 45 27 0 24 May 2022
Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning Antonio Emanuele Cinà Kathrin Grosse Ambra Demontis Sebastiano Vascon Werner Zellinger Bernhard A. Moser Alina Oprea Battista Biggio Marcello Pelillo Fabio Roli AAML 22 116 0 04 May 2022
Indiscriminate Data Poisoning Attacks on Neural Networks Yiwei Lu Gautam Kamath Yaoliang Yu AAML 43 24 0 19 Apr 2022
Poisons that are learned faster are more effective Pedro Sandoval-Segura Vasu Singla Liam H. Fowl Jonas Geiping Micah Goldblum David Jacobs Tom Goldstein 8 17 0 19 Apr 2022
Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets Florian Tramèr Reza Shokri Ayrton San Joaquin Hoang Minh Le Matthew Jagielski Sanghyun Hong Nicholas Carlini MIACV 38 107 0 31 Mar 2022
Robust Unlearnable Examples: Protecting Data Against Adversarial Learning Shaopeng Fu Fengxiang He Yang Liu Li Shen Dacheng Tao 24 24 0 28 Mar 2022
Indiscriminate Poisoning Attacks on Unsupervised Contrastive Learning Hao He Kaiwen Zha Dina Katabi AAML 34 32 0 22 Feb 2022
Learnability Lock: Authorized Learnability Control Through Adversarial Invertible Transformations Weiqi Peng Jinghui Chen AAML 18 5 0 03 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 86 16 0 31 Jan 2022
Identifying a Training-Set Attack's Target Using Renormalized Influence Estimation Zayd Hammoudeh Daniel Lowd TDI 21 28 0 25 Jan 2022
Towards Adversarial Evaluations for Inexact Machine Unlearning Shashwat Goel Ameya Prabhu Amartya Sanyal Ser-Nam Lim Philip Torr Ponnurangam Kumaraguru AAML ELM MU 29 47 0 17 Jan 2022
Going Grayscale: The Road to Understanding and Improving Unlearnable Examples Zhuoran Liu Zhengyu Zhao A. Kolmus Tijn Berns Twan van Laarhoven Tom Heskes Martha Larson AAML 39 6 0 25 Nov 2021
Fooling Adversarial Training with Inducing Noise Zhirui Wang Yifei Wang Yisen Wang 22 14 0 19 Nov 2021
Availability Attacks Create Shortcuts Da Yu Huishuai Zhang Wei Chen Jian Yin Tie-Yan Liu AAML 31 57 0 01 Nov 2021
FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data Eugenia Iofinova Nikola Konstantinov Christoph H. Lampert FaML 36 0 0 22 Jun 2021
Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch Hossein Souri Liam H. Fowl Ramalingam Chellappa Micah Goldblum Tom Goldstein SILM 31 123 0 16 Jun 2021
Disrupting Model Training with Adversarial Shortcuts Ivan Evtimov Ian Covert Aditya Kusupati Tadayoshi Kohno AAML 25 10 0 12 Jun 2021
Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training Lue Tao Lei Feng Jinfeng Yi Sheng-Jun Huang Songcan Chen AAML 34 71 0 09 Feb 2021
Unlearnable Examples: Making Personal Data Unexploitable Hanxun Huang Xingjun Ma S. Erfani James Bailey Yisen Wang MIACV 156 190 0 13 Jan 2021
With False Friends Like These, Who Can Notice Mistakes? Lue Tao Lei Feng Jinfeng Yi Songcan Chen AAML 18 5 0 29 Dec 2020
Towards Class-Oriented Poisoning Attacks Against Neural Networks Bingyin Zhao Yingjie Lao SILM AAML 9 18 0 31 Jul 2020
Disentangling Adversarial Robustness and Generalization David Stutz Matthias Hein Bernt Schiele AAML OOD 191 273 0 03 Dec 2018