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Machine learning models have achieved great success in supervised learning tasks for end-to-end training, which requires a large amount of labeled data that is not always feasible.
“Threats to federated learning: A survey” arXiv preprint arXiv:2003.02133, 2020
Lingjuan Lyu, Han Yu and Qiang Yang · 2003
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Zhou Wang, Eero Simoncelli and Alan Bovik · 2003
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“Exploiting machine learning to subvert your spam filter.”
Blaine Nelson, Marco Barreno, Fuching Chi, Anthony Joseph, Benjamin Rubinstein, Udam Saini, Charles Sutton, J Tygar and Kai Xia · 2008
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“ImageNet: A large-scale hierarchical image database”
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li and Li Fei-Fei · 2009
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“Learning multiple layers of features from tiny images” tech. report, 2009
Alex Krizhevsky · 2009
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“Mining adversarial patterns via regularized loss minimization”
Wei Liu and Sanjay Chawla · 2010
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“Poisoning attacks against support vector machines”
Battista Biggio, Blaine Nelson and Pavel Laskov · 2012
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“Intriguing properties of neural networks” International Conference on Learning Representation, 2014
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow and Rob Fergus · 2014
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“U-net: Convolutional networks for biomedical image segmentation”
Olaf Ronneberger, Philipp Fischer and Thomas Brox · 2015
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“Deep Residual Learning for Image Recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
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“Microsoft chatbot is taught to swear on Twitter”
Jane Wakefield · 2016
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“Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer”
Babak Bejnordi et al · 2017
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“Targeted backdoor attacks on deep learning systems using data poisoning” arXiv:1712.05526, 2017
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu and Dawn Song · 2017
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Tianyu Gu, Brendan Dolan-Gavitt and Siddharth Garg · 2017
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“Understanding black-box predictions via influence functions”
Pang Koh and Percy Liang · 2017
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“Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization”
Luis Muñoz-González, Battista Biggio, Ambra Demontis, Andrea Paudice, Vasin Wongrassamee, Emil. Lupu and Fabio Roli · 2017
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“Representation learning with contrastive predictive coding”
Aaron van Oord, Yazhe Li and Oriol Vinyals · 2018
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“Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks”
Ali Shafahi, W. Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras and Tom Goldstein · 2018
Cited alongside, same era.
“Spectral Signatures in Backdoor Attacks”
Brandon Tran, Jerry Li and Aleksander Madry · 2018
Cited alongside, same era.
“Rotation equivariant CNNs for digital pathology”
Bastiaan Veeling, Jasper Linmans, Jim Winkens, Taco Cohen and Max Welling · 2018
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“Transferable clean-label poisoning attacks on deep neural nets”
Chen Zhu, W Huang, Hengduo Li, Gavin Taylor, Christoph Studer and Tom Goldstein · 2019
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“A Simple Framework for Contrastive Learning of Visual Representations”
Ting Chen, Simon Kornblith, Mohammad Norouzi and Geoffrey Hinton · 2020
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“Big Self-Supervised Models are Strong Semi-Supervised Learners”
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi and Geoffrey Hinton · 2020
Liam Fowl, Ping-yeh Chiang, Micah Goldblum, Jonas Geiping, Arpit Bansal, Wojtek Czaja and Tom Goldstein · 2021
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“Adversarial Examples Make Strong Poisons”
Liam Fowl, Micah Goldblum, Ping-yeh Chiang, Jonas Geiping, Wojciech Czaja and Tom Goldstein · 2021
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“Robust unlearnable examples: Protecting data privacy against adversarial learning”
Shaopeng Fu, Fengxiang He, Yang Liu, Li Shen and Dacheng Tao · 2021
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“Unlearnable Examples: Making Personal Data Unexploitable”
Hanxun Huang, Xingjun Ma, Sarah Erfani, James Bailey and Yisen Wang · 2021
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“Analysis of Loss Functions for Image Reconstruction Using Convolutional Autoencoder”
Nishant Khare, Poornima Thakur, Pritee Khanna and Aparajita Ojha · 2021
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Cited alongside, same era.
“An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale”
Alexey Dosovitskiy et al · 2020
Cited alongside, same era.
Leo Gao et al · 2020
Cited alongside, same era.
“Practical Poisoning Attacks on Neural Networks”
Junfeng Guo and Cong Liu · 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.
“Boosting Contrastive Self-Supervised Learning with False Negative Cancellation”
Tri Huynh, Simon Kornblith, Matthew Walter, Michael Maire and Maryam Khademi · 2020
Cited alongside, same era.
“Adversarial machine learning-industry perspectives”
Ram Kumar, Magnus Nyström, John Lambert, Andrew Marshall, Mario Goertzel, Andi Comissoneru, Matt Swann and Sharon Xia · 2020
Cited alongside, same era.
“Learning transferable visual models from natural language supervision”
Alec Radford et al · 2021
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“Model-targeted poisoning attacks with provable convergence”
Fnu Suya, Saeed Mahloujifar, Anshuman Suri, David Evans and Yuan Tian · 2021
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“How transferable are self-supervised features in medical image classification tasks?”
Tuan Truong, Sadegh Mohammadi and Matthias Lenga · 2021
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“Robustly-reliable learners under poisoning attacks”
Maria-Florina Balcan, Avrim Blum, Steve Hanneke and Dravyansh Sharma · 2022
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“Indiscriminate Poisoning Attacks on Unsupervised Contrastive Learning”
Hao He, Kaiwen Zha and Dina Katabi · 2022
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“Stronger Data Poisoning Attacks Break Data Sanitization Defenses”
Pang Koh, Jacob Steinhardt and Percy Liang · 2022
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“Indiscriminate Data Poisoning Attacks on Neural Networks”
Yiwei Lu, Gautam Kamath and Yaoliang Yu · 2022
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“Autoregressive Perturbations for Data Poisoning”
Pedro Sandoval-Segura, Vasu Singla, Jonas Geiping, Micah Goldblum, Tom Goldstein and David. Jacobs · 2022
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“Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning”
Virat Shejwalkar, Amir Houmansadr, Peter Kairouz and Daniel Ramage · 2022
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“Availability Attacks Create Shortcuts”
Da Yu, Huishuai Zhang, Wei Chen, Jian Yin and Tie-Yan Liu · 2022
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“Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks”
Yiwei Lu, Gautam Kamath and Yaoliang Yu · 2023
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“ f f -MICL: Understanding and Generalizing InfoNCE-based Contrastive Learning”
Yiwei Lu, Guojun Zhang, Sun Sun, Hongyu Guo and Yaoliang Yu · 2023
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