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ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models

Feb 04, 2021
Yugeng Liu, Rui Wen, Xinlei He, Ahmed Salem, Zhikun Zhang, Michael Backes, Emiliano De Cristofaro, Mario Fritz, Yang Zhang


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BAAAN: Backdoor Attacks Against Autoencoder and GAN-Based Machine Learning Models

Oct 08, 2020
Ahmed Salem, Yannick Sautter, Michael Backes, Mathias Humbert, Yang Zhang


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Don't Trigger Me! A Triggerless Backdoor Attack Against Deep Neural Networks

Oct 07, 2020
Ahmed Salem, Michael Backes, Yang Zhang


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BadNL: Backdoor Attacks Against NLP Models

Jun 01, 2020
Xiaoyi Chen, Ahmed Salem, Michael Backes, Shiqing Ma, Yang Zhang


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Dynamic Backdoor Attacks Against Machine Learning Models

Mar 07, 2020
Ahmed Salem, Rui Wen, Michael Backes, Shiqing Ma, Yang Zhang


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MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples

Sep 26, 2019
Jinyuan Jia, Ahmed Salem, Michael Backes, Yang Zhang, Neil Zhenqiang Gong

* To appear in CCS'19 

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Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning

Apr 01, 2019
Ahmed Salem, Apratim Bhattacharya, Michael Backes, Mario Fritz, Yang Zhang


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MLCapsule: Guarded Offline Deployment of Machine Learning as a Service

Aug 01, 2018
Lucjan Hanzlik, Yang Zhang, Kathrin Grosse, Ahmed Salem, Max Augustin, Michael Backes, Mario Fritz


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

Jun 04, 2018
Ahmed Salem, Yang Zhang, Mathias Humbert, Mario Fritz, Michael Backes


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