ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

Authors: Ahmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang, Mario Fritz, Michael Backes | Published: 2018-06-04 | Updated: 2018-12-14
2018.06.04 2025.04.03
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Sufficient Conditions for Idealised Models to Have No Adversarial Examples: a Theoretical and Empirical Study with Bayesian Neural Networks

Authors: Yarin Gal, Lewis Smith | Published: 2018-06-02 | Updated: 2018-06-28
2018.06.02 2025.04.03
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Detecting Adversarial Examples via Key-based Network

Authors: Pinlong Zhao, Zhouyu Fu, Ou wu, Qinghua Hu, Jun Wang | Published: 2018-06-02
2018.06.02 2025.04.03
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Tokenized Data Markets

Authors: Bharath Ramsundar, Roger Chen, Alok Vasudev, Rob Robbins, Artur Gorokh | Published: 2018-05-31
2018.05.31 2025.04.03
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PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks

Authors: Jan Svoboda, Jonathan Masci, Federico Monti, Michael M. Bronstein, Leonidas Guibas | Published: 2018-05-31
2018.05.31 2025.04.03
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Resisting Adversarial Attacks using Gaussian Mixture Variational Autoencoders

Authors: Partha Ghosh, Arpan Losalka, Michael J Black | Published: 2018-05-31 | Updated: 2018-12-10
2018.05.31 2025.04.03
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Defending Against Machine Learning Model Stealing Attacks Using Deceptive Perturbations

Authors: Taesung Lee, Benjamin Edwards, Ian Molloy, Dong Su | Published: 2018-05-31 | Updated: 2018-12-13
2018.05.31 2025.04.03
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Sequential Attacks on Agents for Long-Term Adversarial Goals

Authors: Edgar Tretschk, Seong Joon Oh, Mario Fritz | Published: 2018-05-31 | Updated: 2018-07-05
2018.05.31 2025.04.03
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Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data

Authors: Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan | Published: 2018-05-31
2018.05.31 2025.04.03
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Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks

Authors: Kang Liu, Brendan Dolan-Gavitt, Siddharth Garg | Published: 2018-05-30
2018.05.30 2025.04.03
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