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As a new paradigm in machine learning, self-supervised learning (SSL) is capable of learning high-quality representations of complex data without relying on labels.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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ImageNet: A Large-scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L. Li, Kai Li, and Li Fei-Fei · 2009
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Learning Multiple Layers of Features from Tiny Images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Distributions of angles in random packing on spheres
Tony Cai, Jianqing Fan, and Tiefeng Jiang · 2013
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Comparison of secure and high capacity color image steganography techniques in rgb and ycbcr domains
S Hemalatha, U Dinesh Acharya, and A Renuka · 2013
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A dwt, dct and svd based watermarking technique to protect the image piracy
Md Rahman et al · 2013
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Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size
Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer · 2016
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Improving the robustness of deep neural networks via stability training
Stephan Zheng, Yang Song, Thomas Leung, and Ian Goodfellow · 2016
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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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Badnets: Identifying vulnerabilities in the machine learning model supply chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2017
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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 · 2018
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Model-Reuse Attacks on Deep Learning Systems
Yujie Ji, Xinyang Zhang, Shouling Ji, Xiapu Luo, and Ting Wang · 2018
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Trojaning attack on neural networks
Yingqi Liu, Shiqing Ma, Yousra Aafer, Wen-Chuan Lee, Juan Zhai, Weihang Wang, and Xiangyu Zhang · 2018
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Shufflenet v2: Practical guidelines for efficient cnn architecture design
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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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
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Spectral signatures in backdoor attacks
Brandon Tran, Jerry Li, and Aleksander Madry · 2018
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz · 2018
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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.
Using self-supervised learning can improve model robustness and uncertainty
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song · 2019
Cited alongside, same era.
Neuroninspect: Detecting backdoors in neural networks via output explanations
Xijie Huang, Moustafa Alzantot, and Mani Srivastava · 2019
Cited alongside, same era.
Det: Defending against adversarial examples via decreasing transferability
Changjiang Li, Haiqin Weng, Shouling Ji, Jianfeng Dong, and Qinming He · 2019
Cited alongside, same era.
Abs: Scanning neural networks for back-doors by artificial brain stimulation
Yingqi Liu, Wen-Chuan Lee, Guanhong Tao, Shiqing Ma, Yousra Aafer, and Xiangyu Zhang · 2019
Cited alongside, same era.
Deep probabilistic models to detect data poisoning attacks
Demon in the variant: Statistical analysis of dnns for robust backdoor contamination detection
Di Tang, XiaoFeng Wang, Haixu Tang, and Kehuan Zhang · 2020
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Tongzhou Wang and Phillip Isola · 2020
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Clean-label backdoor attacks on video recognition models
Shihao Zhao, Xingjun Ma, Xiang Zheng, James Bailey, Jingjing Chen, and Yu-Gang Jiang · 2020
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Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2021
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When does contrastive learning preserve adversarial robustness from pretraining to finetuning?
Lijie Fan, Sijia Liu, Pin-Yu Chen, Gaoyuan Zhang, and Chuang Gan · 2021
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Badencoder: Backdoor attacks to pre-trained encoders in self-supervised learning
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Mahesh Subedar, Nilesh Ahuja, Ranganath Krishnan, Ibrahima J Ndiour, and Omesh Tickoo · 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
B. Wang, Y. Yao, S. Shan, H. Li, B. Viswanath, H. Zheng, and B. Y. Zhao · 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.
Latent backdoor attacks on deep neural networks
Yuanshun Yao, Huiying Li, Haitao Zheng, and Ben Y. Zhao · 2019
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Cited alongside, same era.
Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2020
Cited alongside, same era.
Jinyuan Jia, Yupei Liu, and Neil Zhenqiang Gong · 2021
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Towards certifying the asymmetric robustness for neural networks: quantification and applications
Changjiang Li, Shouling Ji, Haiqin Weng, Bo Li, Jie Shi, Raheem Beyah, Shanqing Guo, Zonghui Wang, and Ting Wang · 2021
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Backdoor attacks on self-supervised learning
Aniruddha Saha, Ajinkya Tejankar, Soroush Abbasi Koohpayegani, and Hamed Pirsiavash · 2021
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Excess capacity and backdoor poisoning
Naren Sarayu Manoj and Avrim Blum · 2021
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Detecting ai trojans using meta neural analysis
Xiaojun Xu, Qi Wang, Huichen Li, Nikita Borisov, Carl A Gunter, and Bo Li · 2021
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Poisoning and backdooring contrastive learning
Nicholas Carlini and Andreas Terzis · 2022
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Poisonedencoder: Poisoning the unlabeled pre-training data in contrastive learning
Hongbin Liu, Jinyuan Jia, and Neil Zhenqiang Gong · 2022
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The dark side of automl: Towards architectural backdoor search
Ren Pang, Changjiang Li, Zhaohan Xi, Shouling Ji, and Ting Wang · 2022
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Hijack vertical federated learning models with adversarial embedding
Pengyu Qiu, Xuhong Zhang, Shouling Ji, Changjiang Li, Yuwen Pu, Xing Yang, and Ting Wang · 2022
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An invisible black-box backdoor attack through frequency domain
Tong Wang, Yuan Yao, Feng Xu, Shengwei An, Hanghang Tong, and Ting Wang · 2022
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Chaos is a ladder: A new theoretical understanding of contrastive learning via augmentation overlap
Yifei Wang, Qi Zhang, Yisen Wang, Jiansheng Yang, and Zhouchen Lin · 2022
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Is self-supervised learning more robust than supervised learning?
Yuanyi Zhong, Haoran Tang, Junkun Chen, Jian Peng, and Yu-Xiong Wang · 2022
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Bottrinet: A unified and efficient embedding for social bots detection via metric learning
Jun Wu, Xuesong Ye, and Yanyuet Man · 2023
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On the security risks of knowledge graph reasoning
Zhaohan Xi, Tianyu Du, Changjiang Li, Ren Pang, Shouling Ji, Xiapu Luo, Xusheng Xiao, Fenglong Ma, and Ting Wang · 2023
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