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Recent studies propose membership inference (MI) attacks on deep models, where the goal is to infer if a sample has been used in the training process.
The base-rate fallacy and the difficulty of intrusion detection
Stefan Axelsson · 2000
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.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Earlier work this paper cites.
Xception: Deep learning with depthwise separable convolutions
François Chollet · 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.
Towards measuring membership privacy
Yunhui Long, Vincent Bindschaedler, and Carl A Gunter · 2017
Earlier work this paper cites.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Earlier work this paper cites.
Machine learning models that remember too much
Congzheng Song, Thomas Ristenpart, and Vitaly Shmatikov · 2017
Earlier work this paper cites.
Boosting adversarial attacks with momentum
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li · 2018
Cited alongside, same era.
Empirical study of the topology and geometry of deep networks
Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard, and Stefano Soatto · 2018
Cited alongside, same era.
Understanding membership inferences on well-generalized learning models
Yunhui Long, Vincent Bindschaedler, Lei Wang, Diyue Bu, Xiaofeng Wang, Haixu Tang, Carl A Gunter, and Kai Chen · 2018
Cited alongside, same era.
Classification uncertainty of deep neural networks based on gradient information
Philipp Oberdiek, Matthias Rottmann, and Hanno Gottschalk · 2018
Cited alongside, same era.
Ahmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang, Mario Fritz, and Michael Backes · 2018
Characterizing the decision boundary of deep neural networks
Hamid Karimi, Tyler Derr, and Jiliang Tang · 2019
Later among the works it cites.
Socinf: Membership inference attacks on social media health data with machine learning
Gaoyang Liu, Chen Wang, Kai Peng, Haojun Huang, Yutong Li, and Wenqing Cheng · 2019
Later among the works it cites.
Robustness via curvature regularization, and vice versa
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Jonathan Uesato, and Pascal Frossard · 2019
Later among the works it cites.
Comprehensive privacy analysis of deep learning
Milad Nasr, Reza Shokri, and Amir Houmansadr · 2019
Later among the works it cites.
Privacy risks of securing machine learning models against adversarial examples
Liwei Song, Reza Shokri, and Prateek Mittal · 2019
Later among the works it cites.
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Cited alongside, same era.
A novel intrusion detection model for a massive network using convolutional neural networks
Kehe Wu, Zuge Chen, and Wei Li · 2018
Cited alongside, same era.
Privacy risk in machine learning: Analyzing the connection to overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
Cited alongside, same era.
Memguard: Defending against black-box membership inference attacks via adversarial examples
Jinyuan Jia, Ahmed Salem, Michael Backes, Yang Zhang, and Neil Zhenqiang Gong · 2019
Cited alongside, same era.
The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song
Cited in the paper.
Evaluating differentially private machine learning in practice
Bargav Jayaraman and David Evans
Cited in the paper.
Stolen memories: Leveraging model memorization for calibrated white-box membership inference
Klas Leino and Matt Fredrikson
Cited in the paper.
Stacey Truex, Ling Liu, Mehmet Emre Gursoy, Lei Yu, and Wenqi Wei · 2019
Later among the works it cites.
Understanding the decision boundary of deep neural networks: An empirical study
David Mickisch, Felix Assion, Florens Greßner, Wiebke Günther, and Mariele Motta · 2020
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