Fetching the paper…
Reading the bibliography…
As the curation of data for machine learning becomes increasingly automated, dataset tampering is a mounting threat.
Poisoning attacks against support vector machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
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
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.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
Earlier work this paper cites.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2017
Earlier work this paper cites.
Trojaning attack on neural networks
Yingqi Liu, Shiqing Ma, Yousra Aafer, Wen-Chuan Lee, Juan Zhai, Weihang Wang, and Xiangyu Zhang · 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.
Certified Defenses for Data Poisoning Attacks
Jacob Steinhardt, Pang Wei W Koh, and Percy S Liang · 2017
Earlier work this paper cites.
Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
Earlier work this paper cites.
Poison frogs! targeted clean-label poisoning attacks on neural networks
Ali Shafahi, W Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein · 2018
Earlier work this paper cites.
Spectral signatures in backdoor attacks
Brandon Tran, Jerry Li, and Aleksander Madry · 2018
Earlier work this paper cites.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2018
Cited alongside, same era.
A new backdoor attack in cnns by training set corruption without label poisoning
Mauro Barni, Kassem Kallas, and Benedetta Tondi · 2019
Cited alongside, same era.
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.
Learning to confuse: generating training time adversarial data with auto-encoder
Ji Feng, Qi-Zhi Cai, and Zhi-Hua Zhou · 2019
Cited alongside, same era.
Strip: A defence against trojan attacks on deep neural networks
Yansong Gao, Change Xu, Derui Wang, Shiping Chen, Damith C Ranasinghe, and Surya Nepal · 2019
Cited alongside, same era.
Invisible backdoor attacks on deep neural networks via steganography and regularization
Shaofeng Li, Minhui Xue, Benjamin Zhao, Haojin Zhu, and Xinpeng Zhang · 2020
Later among the works it cites.
Reflection backdoor: A natural backdoor attack on deep neural networks
Yunfei Liu, Xingjun Ma, James Bailey, and Feng Lu · 2020
Later among the works it cites.
Wanet-imperceptible warping-based backdoor attack
Tuan Anh Nguyen and Anh Tuan Tran · 2020
Later among the works it cites.
Hidden trigger backdoor attacks
Aniruddha Saha, Akshayvarun Subramanya, and Hamed Pirsiavash · 2020
Later among the works it cites.
Eitan Borgnia, Jonas Geiping, Valeriia Cherepanova, Liam Fowl, Arjun Gupta, Amin Ghiasi, Furong Huang, Micah Goldblum, and Tom Goldstein · 2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Shaofeng Li, Benjamin Zi Hao Zhao, Jiahao Yu, Minhui Xue, Dali Kaafar, and Haojin Zhu · 2019
Cited alongside, same era.
Tensorclog: An imperceptible poisoning attack on deep neural network applications
Juncheng Shen, Xiaolei Zhu, and De Ma · 2019
Cited alongside, same era.
Label-consistent backdoor attacks
Alexander Turner, Dimitris Tsipras, and Aleksander Madry · 2019
Cited alongside, same era.
Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y Zhao · 2019
Cited alongside, same era.
Transferable clean-label poisoning attacks on deep neural nets
Chen Zhu, W Ronny Huang, Hengduo Li, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2019
Cited alongside, same era.
How to backdoor federated learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov · 2020
Cited alongside, same era.
Practical poisoning attacks on neural networks
Junfeng Guo and Cong Liu · 2020
Cited alongside, same era.
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 · 2021
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 · 2021
Closest in time.
Backdoor attacks against deep learning systems in the physical world
Emily Wenger, Josephine Passananti, Arjun Nitin Bhagoji, Yuanshun Yao, Haitao Zheng, and Ben Y Zhao · 2021
Closest in time.
Adversarial neuron pruning purifies backdoored deep models
Dongxian Wu and Yisen Wang · 2021
Closest in time.
Dataset condensation with gradient matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2021
Closest in time.
Dataset security for machine learning: Data poisoning, backdoor attacks, and defenses
Micah Goldblum, Dimitris Tsipras, Chulin Xie, Xinyun Chen, Avi Schwarzschild, Dawn Song, Aleksander Madry, Bo Li, and Tom Goldstein · 2022
Closest in time.
Backdoor learning: A survey
Yiming Li, Yong Jiang, Zhifeng Li, and Shu-Tao Xia · 2022
Closest in time.