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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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Real-time Analysis of Privacy-(un)aware IoT Applications

Nov 24, 2019
Leonardo Babun, Z. Berkay Celik, Patrick McDaniel, A. Selcuk Uluagac


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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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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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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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On the (Statistical) Detection of Adversarial Examples

Oct 17, 2017
Kathrin Grosse, Praveen Manoharan, Nicolas Papernot, Michael Backes, Patrick McDaniel

* 13 pages, 4 figures, 5 tables. New version: improved writing, incorporating external feedback 

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Patient-Driven Privacy Control through Generalized Distillation

Oct 13, 2017
Z. Berkay Celik, David Lopez-Paz, Patrick McDaniel

* IEEE Symposium on Privacy-Aware Computing (IEEE PAC), 2017 

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The Space of Transferable Adversarial Examples

May 23, 2017
Florian Tramèr, Nicolas Papernot, Ian Goodfellow, Dan Boneh, Patrick McDaniel

* 15 pages, 7 figures 

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Extending Defensive Distillation

May 15, 2017
Nicolas Papernot, Patrick McDaniel


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Practical Black-Box Attacks against Machine Learning

Mar 19, 2017
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z. Berkay Celik, Ananthram Swami

* Proceedings of the 2017 ACM Asia Conference on Computer and Communications Security, Abu Dhabi, UAE 

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Towards the Science of Security and Privacy in Machine Learning

Nov 11, 2016
Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, Michael Wellman


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Adversarial Perturbations Against Deep Neural Networks for Malware Classification

Jun 16, 2016
Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, Patrick McDaniel

* version update: correcting typos, incorporating external feedback 

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Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples

May 24, 2016
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow


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Crafting Adversarial Input Sequences for Recurrent Neural Networks

Apr 28, 2016
Nicolas Papernot, Patrick McDaniel, Ananthram Swami, Richard Harang


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Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks

Mar 14, 2016
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, Ananthram Swami


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The Limitations of Deep Learning in Adversarial Settings

Nov 24, 2015
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, Ananthram Swami

* Accepted to the 1st IEEE European Symposium on Security & Privacy, IEEE 2016. Saarbrucken, Germany 

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