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Data-poisoning based backdoor attacks aim to insert backdoor into models by manipulating training datasets without controlling the training process of the target model.
Learning representations by back-propagating errors
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1986
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Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
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The tradeoffs of large scale learning
Léon Bottou and Olivier Bousquet · 2007
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Yiming Li, Baoyuan Wu, Yong Jiang, Zhifeng Li, and Shu-Tao Xia · 2007
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Tiny imagenet visual recognition challenge
Ya Le and Xuan S. Yang · 2015
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Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Active bias: Training more accurate neural networks by emphasizing high variance samples
Haw-Shiuan Chang, Erik Learned-Miller, and Andrew McCallum · 2017
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Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2017
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Not all samples are created equal: Deep learning with importance sampling
Angelos Katharopoulos and François Fleuret · 2018
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An empirical study of example forgetting during deep neural network learning
Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J Gordon · 2018
Earlier work this paper cites.
Spectral signatures in backdoor attacks
Brandon Tran, Jerry Li, and Aleksander Madry · 2018
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A new backdoor attack in cnns by training set corruption without label poisoning
Mauro Barni, Kassem Kallas, and Benedetta Tondi · 2019
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Detecting backdoor attacks on deep neural networks by activation clustering
Bryant Chen, Wilka Carvalho, Nathalie Baracaldo, Heiko Ludwig, Benjamin Edwards, Taesung Lee, Ian Molloy, and Biplav Srivastava · 2019
Cited alongside, same era.
Selection via proxy: Efficient data selection for deep learning
Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, and Matei Zaharia · 2019
Cited alongside, same era.
Badnets: Evaluating backdooring attacks on deep neural networks
Tianyu Gu, Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg · 2019
Cited alongside, same era.
Learning from less data: A unified data subset selection and active learning framework for computer vision
Vishal Kaushal, Rishabh Iyer, Suraj Kothawade, Rohan Mahadev, Khoshrav Doctor, and Ganesh Ramakrishnan · 2019
Cited alongside, same era.
Label-consistent backdoor attacks
Alexander Turner, Dimitris Tsipras, and Aleksander Madry · 2019
Backdoor defense via decoupling the training process
Kunzhe Huang, Yiming Li, Baoyuan Wu, Zhan Qin, and Kui Ren · 2022
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Dynamic backdoor attacks against machine learning models
Ahmed Salem, Rui Wen, Michael Backes, Shiqing Ma, and Yang Zhang · 2022
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Bppattack: Stealthy and efficient trojan attacks against deep neural networks via image quantization and contrastive adversarial learning
Zhenting Wang, Juan Zhai, and Shiqing Ma · 2022
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Backdoorbench: A comprehensive benchmark of backdoor learning
Baoyuan Wu, Hongrui Chen, Mingda Zhang, Zihao Zhu, Shaokui Wei, Danni Yuan, and Chao Shen · 2022
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Data-efficient backdoor attacks
Pengfei Xia, Ziqiang Li, Wei Zhang, and Bin Li · 2022
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Narcissus: A practical clean-label backdoor attack with limited information
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Cited alongside, same era.
Coresets via bilevel optimization for continual learning and streaming
Zalán Borsos, Mojmir Mutny, and Andreas Krause · 2020
Cited alongside, same era.
Coresets for data-efficient training of machine learning models
Baharan Mirzasoleiman, Jeff Bilmes, and Jure Leskovec · 2020
Cited alongside, same era.
Input-aware dynamic backdoor attack
Tuan Anh Nguyen and Anh Tran · 2020
Cited alongside, same era.
Data valuation using reinforcement learning
Jinsung Yoon, Sercan Arik, and Tomas Pfister · 2020
Cited alongside, same era.
Wanet - imperceptible warping-based backdoor attack
Tuan Anh Nguyen and Anh Tuan Tran · 2021
Cited alongside, same era.
Deep learning on a data diet: Finding important examples early in training
Mansheej Paul, Surya Ganguli, and Gintare Karolina Dziugaite · 2021
Cited alongside, same era.
Sleeper agent: Scalable hidden trigger backdoors for neural networks trained from scratch
Hossein Souri, Micah Goldblum, Liam Fowl, Rama Chellappa, and Tom Goldstein · 2021
Cited alongside, same era.
Yi Zeng, Minzhou Pan, Hoang Anh Just, Lingjuan Lyu, Meikang Qiu, and Ruoxi Jia · 2022
Later among the works it cites.
Poison ink: Robust and invisible backdoor attack
Jie Zhang, Chen Dongdong, Qidong Huang, Jing Liao, Weiming Zhang, Huamin Feng, Gang Hua, and Nenghai Yu · 2022
Later among the works it cites.
Data-free backdoor removal based on channel lipschitzness
Runkai Zheng, Rongjun Tang, Jianze Li, and Li Liu · 2022
Later among the works it cites.
Defending backdoor attacks on vision transformer via patch processing
Khoa D Doan, Yingjie Lao, and Ping Li · 2023
Closest in time.
LAVA: Data valuation without pre-specified learning algorithms
Hoang Anh Just, Feiyang Kang, Tianhao Wang, Yi Zeng, Myeongseob Ko, Ming Jin, and Ruoxi Jia · 2023
Closest in time.
Data valuation without training of a model
Ki Nohyun, Hoyong Choi, and Hye Won Chung · 2023
Closest in time.
Baoyuan Wu, Li Liu, Zihao Zhu, Qingshan Liu, Zhaofeng He, and Siwei Lyu · 2023
Closest in time.
How to sift out a clean data subset in the presence of data poisoning?
Yi Zeng, Minzhou Pan, Himanshu Jahagirdar, Ming Jin, Lingjuan Lyu, and Ruoxi Jia · 2023
Closest in time.
Enhancing fine-tuning based backdoor defense with sharpness-aware minimization
Mingli Zhu, Shaokui Wei, Li Shen, Yanbo Fan, and Baoyuan Wu · 2023
Closest in time.