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Unsolved Problems in ML Safety


Sep 28, 2021
Dan Hendrycks, Nicholas Carlini, John Schulman, Jacob Steinhardt

* Position Paper 

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Deduplicating Training Data Makes Language Models Better


Jul 14, 2021
Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, Nicholas Carlini


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Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent


Jun 28, 2021
Oliver Bryniarski, Nabeel Hingun, Pedro Pachuca, Vincent Wang, Nicholas Carlini


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Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples


Jun 18, 2021
Maura Pintor, Luca Demetrio, Angelo Sotgiu, Giovanni Manca, Ambra Demontis, Nicholas Carlini, Battista Biggio, Fabio Roli


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Poisoning and Backdooring Contrastive Learning


Jun 17, 2021
Nicholas Carlini, Andreas Terzis


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AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation


Jun 08, 2021
David Berthelot, Rebecca Roelofs, Kihyuk Sohn, Nicholas Carlini, Alex Kurakin


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Handcrafted Backdoors in Deep Neural Networks


Jun 08, 2021
Sanghyun Hong, Nicholas Carlini, Alexey Kurakin

* 16 pages, 13 figures, 11 tables 

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Poisoning the Unlabeled Dataset of Semi-Supervised Learning


May 04, 2021
Nicholas Carlini


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Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning


Jan 11, 2021
Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Nicholas Carlini


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Extracting Training Data from Large Language Models


Dec 14, 2020
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, Alina Oprea, Colin Raffel


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An Attack on InstaHide: Is Private Learning Possible with Instance Encoding?


Nov 10, 2020
Nicholas Carlini, Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Shuang Song, Abhradeep Thakurta, Florian Tramer


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Erratum Concerning the Obfuscated Gradients Attack on Stochastic Activation Pruning


Sep 30, 2020
Guneet S. Dhillon, Nicholas Carlini


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A Partial Break of the Honeypots Defense to Catch Adversarial Attacks


Sep 23, 2020
Nicholas Carlini


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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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Measuring Robustness to Natural Distribution Shifts in Image Classification


Jul 01, 2020
Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, Ludwig Schmidt


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Evading Deepfake-Image Detectors with White- and Black-Box Attacks


Apr 01, 2020
Nicholas Carlini, Hany Farid


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Cryptanalytic Extraction of Neural Network Models


Mar 10, 2020
Nicholas Carlini, Matthew Jagielski, Ilya Mironov


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On Adaptive Attacks to Adversarial Example Defenses


Feb 19, 2020
Florian Tramer, Nicholas Carlini, Wieland Brendel, Aleksander Madry


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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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FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence


Jan 21, 2020
Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Han Zhang, Colin Raffel


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ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring


Nov 21, 2019
David Berthelot, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, Colin Raffel


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


Oct 29, 2019
Nicholas Carlini, Úlfar Erlingsson, Nicolas Papernot


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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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Stateful Detection of Black-Box Adversarial Attacks


Jul 12, 2019
Steven Chen, Nicholas Carlini, David Wagner


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A critique of the DeepSec Platform for Security Analysis of Deep Learning Models


May 17, 2019
Nicholas Carlini


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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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SysML: The New Frontier of Machine Learning Systems


May 01, 2019
Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Jennifer Chayes, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konečný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Aparna Lakshmiratan, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Murray, Kunle Olukotun, Dimitris Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar


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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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Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition


Mar 22, 2019
Yao Qin, Nicholas Carlini, Ian Goodfellow, Garrison Cottrell, Colin Raffel


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