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Data poisoning and backdoor attacks manipulate victim models by maliciously modifying training data.
“ANTIDOTE: Understanding and Defending Against Poisoning of Anomaly Detectors,”
Benjamin I.P. Rubinstein, Blaine Nelson, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Satish Rao, Nina Taft, and J. D. Tygar, · 2009
Earlier work this paper cites.
“Learning multiple layers of features from tiny images,”
Alex Krizhevsky, Geoffrey Hinton, et al., · 2009
Earlier work this paper cites.
“DeepFace: Closing the Gap to Human-Level Performance in Face Verification,”
Yaniv Taigman, Ming Yang, Marc’Aurelio Ranzato, and Lior Wolf, · 2014
Earlier work this paper cites.
“Deep Residual Learning for Image Recognition,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2015
Earlier work this paper cites.
“Deep Learning with Differential Privacy,”
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang, · 2016
Earlier work this paper cites.
“mixup: Beyond empirical risk minimization,”
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz, · 2017
Earlier work this paper cites.
“Improved regularization of convolutional neural networks with cutout,”
Terrance DeVries and Graham W Taylor, · 2017
Earlier work this paper cites.
“Towards Deep Learning Models Resistant to Adversarial Attacks,”
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu, · 2017
Earlier work this paper cites.
“Mixup: Beyond Empirical Risk Minimization,”
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz, · 2017
Earlier work this paper cites.
“Improved Regularization of Convolutional Neural Networks with Cutout,”
Terrance DeVries and Graham W. Taylor, · 2017
Cited alongside, same era.
“Poisoning Attacks to Graph-Based Recommender Systems,”
Minghong Fang, Guolei Yang, Neil Zhenqiang Gong, and Jia Liu, · 2018
Cited alongside, same era.
“A Marauder’s Map of Security and Privacy in Machine Learning,”
Nicolas Papernot, · 2018
Cited alongside, same era.
“Deepisp: Toward learning an end-to-end image processing pipeline,”
Eli Schwartz, Raja Giryes, and Alex M. Bronstein, · 2019
Cited alongside, same era.
“Biometric Backdoors: A Poisoning Attack Against Unsupervised Template Updating,”
Giulio Lovisotto, Simon Eberz, and Ivan Martinovic, · 2019
Cited alongside, same era.
“Deep k-nn defense against clean-label data poisoning attacks,” 2019
Neehar Peri, Neal Gupta, W. Ronny Huang, Liam Fowl, Chen Zhu, Soheil Feizi, Tom Goldstein, and John P. Dickerson, · 2019
“Witches’ brew: Industrial scale data poisoning via gradient matching,”
Jonas Geiping, Liam Fowl, W Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, and Tom Goldstein, · 2020
Closest in time.
“Adversarial Machine Learning – Industry Perspectives,”
Ram Shankar Siva Kumar, Magnus Nyström, John Lambert, Andrew Marshall, Mario Goertzel, Andi Comissoneru, Matt Swann, and Sharon Xia, · 2020
Closest in time.
“Just how toxic is data poisoning? a unified benchmark for backdoor and data poisoning attacks,”
Avi Schwarzschild, Micah Goldblum, Arjun Gupta, John P Dickerson, and Tom Goldstein, · 2020
Closest in time.
“On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping,”
Sanghyun Hong, Varun Chandrasekaran, Yiğitcan Kaya, Tudor Dumitraş, and Nicolas Papernot, · 2020
Closest in time.
“Maxup: A simple way to improve generalization of neural network training,”
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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.
“Metapoison: Practical general-purpose clean-label data poisoning,”
W Ronny Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, and Tom Goldstein, · 2020
Cited alongside, same era.
Chengyue Gong, Tongzheng Ren, Mao Ye, and Qiang Liu, · 2020
Closest in time.
“Data augmentation for meta-learning,” 2020
Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, and Tom Goldstein, · 2020
Closest in time.
“Witchcraft: Efficient pgd attacks with random step size,”
Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum, Tom Goldstein, Renkun Ni, Steven Reich, and Ali Shafahi, · 2020
Closest in time.
“Headless horseman: Adversarial attacks on transfer learning models,”
Ahmed Abdelkader, Michael J Curry, Liam Fowl, Tom Goldstein, Avi Schwarzschild, Manli Shu, Christoph Studer, and Chen Zhu, · 2020
Closest in time.