A new Backdoor Attack in CNNs by training set corruption without label poisoning

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“Gradient-based learning applied to document recognition,”
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner, · 1998
Earlier work this paper cites.
“Traffic sign recognition with multi-scale convolutional networks,”
Pierre Sermanet and Yann LeCun, · 2011
Earlier work this paper cites.
“Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark,”
Original
Sebastian Houben, Johannes Stallkamp, Jan Salmen, Marc Schlipsing, and Christian Igel, · 2013
Earlier work this paper cites.
“Badnets: Identifying vulnerabilities in the machine learning model supply chain,”
Original
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg, · 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
Cited alongside, same era.
“Generative poisoning attack method against neural networks,”
Original
Chaofei Yang, Qing Wu, Hai Li, and Yiran Chen, · 2017
Cited alongside, same era.
“Targeted backdoor attacks on deep learning systems using data poisoning,”
Original
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song, · 2017
Cited alongside, same era.

“Backdoor attacks against learning systems,”
Yujie Ji, Xinyang Zhang, and Ting Wang, · 2017
Later among the works it cites.
“Trojaning attack on neural networks,”
Yingqi Liu, Shiqing Ma, Yousra Aafer, Wen-Chuan Lee, Juan Zhai, Weihang Wang, and Xiangyu Zhang, · 2018
Later among the works it cites.
“Backdoor embedding in convolutional neural network models via invisible perturbation,”
Original
Cong Liao, Haoti Zhong, Anna Squicciarini, Sencun Zhu, and David Miller, · 2018
Later among the works it cites.