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Indiscriminate data poisoning attacks are quite effective against supervised learning.
Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2003
Earlier work this paper cites.
Can machine learning be secure?
Marco Barreno, Blaine Nelson, Russell Sears, Anthony D Joseph, and J Doug Tygar · 2006
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
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Poisoning attacks against support vector machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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Matrix estimation by universal singular value thresholding
Sourav Chatterjee · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio · 2017
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Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
Earlier work this paper cites.
Towards poisoning of deep learning algorithms with back-gradient optimization
Luis Muñoz-González, Battista Biggio, Ambra Demontis, Andrea Paudice, Vasin Wongrassamee, Emil C Lupu, and Fabio Roli · 2017
Earlier work this paper cites.
Wild patterns: Ten years after the rise of adversarial machine learning
Battista Biggio and Fabio Roli · 2018
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Earlier work this paper cites.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2018
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Learning to confuse: generating training time adversarial data with auto-encoder
Ji Feng, Qi-Zhi Cai, and Zhi-Hua Zhou · 2019
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How photos of your kids are powering surveillance technology
Kashmir Hill and Aaron Krolik · 2019
Earlier work this paper cites.
Tensorclog: An imperceptible poisoning attack on deep neural network applications
Juncheng Shen, Xiaolei Zhu, and De Ma · 2019
Cited alongside, same era.
Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
Cited alongside, same era.
Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan · 2019
Cited alongside, same era.
Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
Cited alongside, same era.
Lowkey: leveraging adversarial attacks to protect social media users from facial recognition
Valeriia Cherepanova, Micah Goldblum, Harrison Foley, Shiyuan Duan, John Dickerson, Gavin Taylor, and Tom Goldstein · 2021
Later among the works it cites.
When does contrastive learning preserve adversarial robustness from pretraining to finetuning?
Lijie Fan, Sijia Liu, Pin-Yu Chen, Gaoyuan Zhang, and Chuang Gan · 2021
Later among the works it cites.
What doesn’t kill you makes you robust (er): Adversarial training against poisons and backdoors
Jonas Geiping, Liam Fowl, Gowthami Somepalli, Micah Goldblum, Michael Moeller, and Tom Goldstein · 2021
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Unlearnable examples: Making personal data unexploitable
Hanxun Huang, Xingjun Ma, Sarah Monazam Erfani, James Bailey, and Yisen Wang · 2021
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A survey on contrastive self-supervised learning
Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Debapriya Banerjee, and Fillia Makedon · 2021
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Sanghyun Hong, Varun Chandrasekaran, Yiğitcan Kaya, Tudor Dumitraş, and Nicolas Papernot · 2020
Cited alongside, same era.
Robust pre-training by adversarial contrastive learning
Ziyu Jiang, Tianlong Chen, Ting Chen, and Zhangyang Wang · 2020
Cited alongside, same era.
Adversarial self-supervised contrastive learning
Minseon Kim, Jihoon Tack, and Sung Ju Hwang · 2020
Cited alongside, same era.
Fawkes: Protecting privacy against unauthorized deep learning models
Shawn Shan, Emily Wenger, Jiayun Zhang, Huiying Li, Haitao Zheng, and Ben Y Zhao · 2020
Cited alongside, same era.
Systematic evaluation of backdoor data poisoning attacks on image classifiers
Loc Truong, Chace Jones, Brian Hutchinson, Andrew August, Brenda Praggastis, Robert Jasper, Nicole Nichols, and Aaron Tuor · 2020
Cited alongside, same era.
Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Tongzhou Wang and Phillip Isola · 2020
Cited alongside, same era.
Big self-supervised models advance medical image classification
Shekoofeh Azizi, Basil Mustafa, Fiona Ryan, Zachary Beaver, Jan Freyberg, Jonathan Deaton, Aaron Loh, Alan Karthikesalingam, Simon Kornblith, Ting Chen, et al · 2021
Cited alongside, same era.
Large image datasets: A pyrrhic win for computer vision?
Vinay Uday Prabhu and Abeba Birhane · 2021
Later among the works it cites.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Later among the works it cites.
Backdoor attacks on self-supervised learning
Aniruddha Saha, Ajinkya Tejankar, Soroush Abbasi Koohpayegani, and Hamed Pirsiavash · 2021
Later among the works it cites.
Better safe than sorry: Preventing delusive adversaries with adversarial training
Lue Tao, Lei Feng, Jinfeng Yi, Sheng-Jun Huang, and Songcan Chen · 2021
Later among the works it cites.
Divide and contrast: Self-supervised learning from uncurated data
Yonglong Tian, Olivier J Henaff, and Aäron van den Oord · 2021
Later among the works it cites.
Neural tangent generalization attacks
Chia-Hung Yuan and Shan-Hung Wu · 2021
Later among the works it cites.
Poisoning and backdooring contrastive learning
Nicholas Carlini and Andreas Terzis · 2022
Closest in time.
BadEncoder: Backdoor attacks to pre-trained encoders in self-supervised learning
Jinyuan Jia, Yupei Liu, and Neil Zhenqiang Gong · 2022
Closest in time.
Poisonedencoder: Poisoning the unlabeled pre-training data in contrastive learning
Hongbin Liu, Jinyuan Jia, and Neil Zhenqiang Gong · 2022
Closest in time.
Availability attacks create shortcuts
Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, and Tie-Yan Liu · 2022
Closest in time.