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Adversarial Examples in Constrained Domains

Nov 02, 2020
Ryan Sheatsley, Nicolas Papernot, Michael Weisman, Gunjan Verma, Patrick McDaniel

* 17 pages, 5 figures 

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Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings

Oct 13, 2020
Vinith M. Suriyakumar, Nicolas Papernot, Anna Goldenberg, Marzyeh Ghassemi


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Not My Deepfake: Towards Plausible Deniability for Machine-Generated Media

Aug 20, 2020
Baiwu Zhang, Jin Peng Zhou, Ilia Shumailov, Nicolas Papernot


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Label-Only Membership Inference Attacks

Jul 28, 2020
Christopher A. Choquette Choo, Florian Tramer, Nicholas Carlini, Nicolas Papernot

* 16 pages, 11 figures, 2 tables 

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Tempered Sigmoid Activations for Deep Learning with Differential Privacy

Jul 28, 2020
Nicolas Papernot, Abhradeep Thakurta, Shuang Song, Steve Chien, √ölfar Erlingsson


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SoK: The Faults in our ASRs: An Overview of Attacks against Automatic Speech Recognition and Speaker Identification Systems

Jul 21, 2020
Hadi Abdullah, Kevin Warren, Vincent Bindschaedler, Nicolas Papernot, Patrick Traynor


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The Faults in our ASRs: An Overview of Attacks against Automatic Speech Recognition and Speaker Identification Systems

Jul 13, 2020
Hadi Abdullah, Kevin Warren, Vincent Bindschaedler, Nicolas Papernot, Patrick Traynor


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Sponge Examples: Energy-Latency Attacks on Neural Networks

Jun 05, 2020
Ilia Shumailov, Yiren Zhao, Daniel Bates, Nicolas Papernot, Robert Mullins, Ross Anderson


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On the Robustness of Cooperative Multi-Agent Reinforcement Learning

Mar 08, 2020
Jieyu Lin, Kristina Dzeparoska, Sai Qian Zhang, Alberto Leon-Garcia, Nicolas Papernot


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On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping

Feb 27, 2020
Sanghyun Hong, Varun Chandrasekaran, Yińüitcan Kaya, Tudor DumitraŇü, Nicolas Papernot


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Entangled Watermarks as a Defense against Model Extraction

Feb 27, 2020
Hengrui Jia, Christopher A. Choquette-Choo, Nicolas Papernot


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Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations

Feb 11, 2020
Florian Tramèr, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Jörn-Henrik Jacobsen

* Supersedes the workshop paper "Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness" (arXiv:1903.10484) 

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Machine Unlearning

Dec 09, 2019
Lucas Bourtoule, Varun Chandrasekaran, Christopher Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, Nicolas Papernot


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Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applications

Oct 29, 2019
Nicholas Carlini, √ölfar Erlingsson, Nicolas Papernot


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Thieves on Sesame Street! Model Extraction of BERT-based APIs

Oct 27, 2019
Kalpesh Krishna, Gaurav Singh Tomar, Ankur P. Parikh, Nicolas Papernot, Mohit Iyyer

* preprint, 18 pages 

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Improving Differentially Private Models with Active Learning

Oct 02, 2019
Zhengli Zhao, Nicolas Papernot, Sameer Singh, Neoklis Polyzotis, Augustus Odena


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High-Fidelity Extraction of Neural Network Models

Sep 03, 2019
Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, Nicolas Papernot


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How Relevant is the Turing Test in the Age of Sophisbots?

Aug 30, 2019
Dan Boneh, Andrew J. Grotto, Patrick McDaniel, Nicolas Papernot


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MixMatch: A Holistic Approach to Semi-Supervised Learning

May 06, 2019
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel


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Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness

Mar 25, 2019
Jörn-Henrik Jacobsen, Jens Behrmannn, Nicholas Carlini, Florian Tramèr, Nicolas Papernot

* Accepted at the ICLR 2019 SafeML Workshop 

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On Evaluating Adversarial Robustness

Feb 20, 2019
Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian Goodfellow, Aleksander Madry, Alexey Kurakin

* Living document; source available at https://github.com/evaluating-adversarial-robustness/adv-eval-paper/ 

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Analyzing and Improving Representations with the Soft Nearest Neighbor Loss

Feb 05, 2019
Nicholas Frosst, Nicolas Papernot, Geoffrey Hinton


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Adversarial Vision Challenge

Aug 06, 2018
Wieland Brendel, Jonas Rauber, Alexey Kurakin, Nicolas Papernot, Behar Veliqi, Marcel Salathé, Sharada P. Mohanty, Matthias Bethge

* https://www.crowdai.org/challenges/adversarial-vision-challenge 

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Ensemble Adversarial Training: Attacks and Defenses

Jul 22, 2018
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, Patrick McDaniel

* 20 pages, 5 figures, International Conference on Learning Representations (ICLR) 2018 

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Technical Report on the CleverHans v2.1.0 Adversarial Examples Library

Jun 27, 2018
Nicolas Papernot, Fartash Faghri, Nicholas Carlini, Ian Goodfellow, Reuben Feinman, Alexey Kurakin, Cihang Xie, Yash Sharma, Tom Brown, Aurko Roy, Alexander Matyasko, Vahid Behzadan, Karen Hambardzumyan, Zhishuai Zhang, Yi-Lin Juang, Zhi Li, Ryan Sheatsley, Abhibhav Garg, Jonathan Uesato, Willi Gierke, Yinpeng Dong, David Berthelot, Paul Hendricks, Jonas Rauber, Rujun Long, Patrick McDaniel

* Technical report for https://github.com/tensorflow/cleverhans 

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Adversarial Examples that Fool both Computer Vision and Time-Limited Humans

May 22, 2018
Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alex Kurakin, Ian Goodfellow, Jascha Sohl-Dickstein


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Detection under Privileged Information

Mar 31, 2018
Z. Berkay Celik, Patrick McDaniel, Rauf Izmailov, Nicolas Papernot, Ryan Sheatsley, Raquel Alvarez, Ananthram Swami

* A short version of this paper is accepted to ASIACCS 2018 

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Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning

Mar 13, 2018
Nicolas Papernot, Patrick McDaniel


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Scalable Private Learning with PATE

Feb 24, 2018
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, √ölfar Erlingsson

* Published as a conference paper at ICLR 2018 

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