Fetching the paper…
Reading the bibliography…
Recent studies have revealed that deep neural networks (DNNs) are vulnerable to backdoor attacks, where attackers embed hidden backdoors in the DNN model by poisoning a few training samples.
Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2003
Earlier work this paper cites.
Yiming Li, Baoyuan Wu, Yong Jiang, Zhifeng Li, and Shu-Tao Xia · 2007
Earlier work this paper cites.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Semi-supervised learning with ladder networks
Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, and Tapani Raiko · 2015
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.
Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 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.
Semi-supervised sparse representation based classification for face recognition with insufficient labeled samples
Yuan Gao, Jiayi Ma, and Alan L Yuille · 2017
Earlier work this paper cites.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Earlier work this paper cites.
Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2017
Earlier work this paper cites.
Vggface2: A dataset for recognising faces across pose and age
Qiong Cao, Li Shen, Weidi Xie, Omkar M Parkhi, and Andrew Zisserman · 2018
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Earlier work this paper cites.
Mixed precision training
Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, et al · 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
Earlier work this paper cites.
Generalized cross entropy loss for training deep neural networks with noisy labels
Zhilu Zhang and Mert R Sabuncu · 2018
Earlier work this paper cites.
Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel · 2019
Earlier work this paper cites.
Unlabeled data improves adversarial robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, and John C Duchi · 2019
Earlier work this paper cites.
Gpu accelerated t-distributed stochastic neighbor embedding
David M Chan, Roshan Rao, Forrest Huang, and John F Canny · 2019
Earlier work this paper cites.
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.
Badnets: Evaluating backdooring attacks on deep neural networks
Tianyu Gu, Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg · 2019
Cited alongside, same era.
Using self-supervised learning can improve model robustness and uncertainty
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song · 2019
Cited alongside, same era.
Revisiting self-supervised visual representation learning
Alexander Kolesnikov, Xiaohua Zhai, and Lucas Beyer · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
Adaptive variance based label distribution learning for facial age estimation
Xin Wen, Biying Li, Haiyun Guo, Zhiwei Liu, and Guosheng Hu · 2020
Later among the works it cites.
One-to-n & n-to-one: Two advanced backdoor attacks against deep learning models
Mingfu Xue, Can He, Jian Wang, and Weiqiang Liu · 2020
Later among the works it cites.
Confidence scores make instance-dependent label-noise learning possible
Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, and Masashi Sugiyama · 2021
Later among the works it cites.
Strong data augmentation sanitizes poisoning and backdoor attacks without an accuracy tradeoff
Eitan Borgnia, Valeriia Cherepanova, Liam Fowl, Amin Ghiasi, Jonas Geiping, Micah Goldblum, Tom Goldstein, and Arjun Gupta · 2021
Later among the works it cites.
Poisoning and backdooring contrastive learning
Nicholas Carlini and Andreas Terzis · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ximing Qiao, Yukun Yang, and Hai Li · 2019
Cited alongside, same era.
Are labels required for improving adversarial robustness?
Robert Stanforth, Alhussein Fawzi, Pushmeet Kohli, et al · 2019
Cited alongside, same era.
Label-consistent backdoor attacks
Alexander Turner, Dimitris Tsipras, and Aleksander Madry · 2019
Cited alongside, same era.
Februus: Input purification defense against trojan attacks on deep neural network systems
Bao Gia Doan, Ehsan Abbasnejad, and Damith C Ranasinghe · 2020
Cited alongside, same era.
Robust anomaly detection and backdoor attack detection via differential privacy
Min Du, Ruoxi Jia, and Dawn Song · 2020
Cited alongside, same era.
Consistency-based semi-supervised active learning: Towards minimizing labeling cost
Mingfei Gao, Zizhao Zhang, Guo Yu, Sercan Ö Arık, Larry S Davis, and Tomas Pfister · 2020
Cited alongside, same era.
Bootstrap your own latent - a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, koray kavukcuoglu, Remi Munos, and Michal Valko · 2020
Cited alongside, same era.
Black-box detection of backdoor attacks with limited information and data
Yinpeng Dong, Xiao Yang, Zhijie Deng, Tianyu Pang, Zihao Xiao, Hang Su, and Jun Zhu · 2021
Later among the works it cites.
Badencoder: Backdoor attacks to pre-trained encoders in self-supervised learning
Jinyuan Jia, Yupei Liu, and Neil Zhenqiang Gong · 2021
Later among the works it cites.
Self-supervised learning: Generative or contrastive
Xiao Liu, Fanjin Zhang, Zhenyu Hou, Li Mian, Zhaoyu Wang, Jing Zhang, and Jie Tang · 2021
Later among the works it cites.
Wanet–imperceptible warping-based backdoor attack
Anh Nguyen and Anh Tran · 2021
Later among the works it cites.
Backdoor attacks on self-supervised learning
Aniruddha Saha, Ajinkya Tejankar, Soroush Abbasi Koohpayegani, and Hamed Pirsiavash · 2021
Later among the works it cites.
Backdoor scanning for deep neural networks through k-arm optimization
Guangyu Shen, Yingqi Liu, Guanhong Tao, Shengwei An, Qiuling Xu, Siyuan Cheng, Shiqing Ma, and Xiangyu Zhang · 2021
Later among the works it cites.
Online adversarial purification based on self-supervision
Changhao Shi, Chester Holtz, and Gal Mishne · 2021
Later among the works it cites.
Adversarial defense for automatic speaker verification by cascaded self-supervised learning models
Haibin Wu, Xu Li, Andy T Liu, Zhiyong Wu, Helen Meng, and Hung-yi Lee · 2021
Later among the works it cites.
A backdoor attack against 3d point cloud classifiers
Zhen Xiang, David J Miller, Siheng Chen, Xi Li, and George Kesidis · 2021
Later among the works it cites.
Detecting ai trojans using meta neural analysis
Xiaojun Xu, Qi Wang, Huichen Li, Nikita Borisov, Carl A Gunter, and Bo Li · 2021
Later among the works it cites.
Dehib: Deep hidden backdoor attack on semi-supervised learning via adversarial perturbation
Zhicong Yan, Gaolei Li, Yuan TIan, Jun Wu, Shenghong Li, Mingzhe Chen, and H Vincent Poor · 2021
Later among the works it cites.
Backdoor attack against speaker verification
Tongqing Zhai, Yiming Li, Ziqi Zhang, Baoyuan Wu, Yong Jiang, and Shu-Tao Xia · 2021
Later among the works it cites.
Few-shot backdoor attacks on visual object tracking
Yiming Li, Haoxiang Zhong, Xingjun Ma, Yong Jiang, and Shu-Tao Xia · 2022
Closest in time.
Post-training detection of backdoor attacks for two-class and multi-attack scenarios
Zhen Xiang, David J. Miller, and George Kesidis · 2022
Closest in time.
Adversarial unlearning of backdoors via implicit hypergradient
Yi Zeng, Si Chen, Won Park Z. Morley Mao, Ming Jin, and Ruoxi Jia · 2022
Closest in time.