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ORCIDNicholas Carlini
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2020 – today
- 2021
[c33]Nicholas Carlini:
Session details: Session 1: Adversarial Machine Learning. AISec@CCS 2021- [c32]Nicholas Carlini:
Session details: Session 2A: Machine Learning for Cybersecurity. AISec@CCS 2021 - [c31]Christopher A. Choquette-Choo, Florian Tramèr, Nicholas Carlini, Nicolas Papernot:
Label-Only Membership Inference Attacks. ICML 2021: 1964-1974 - [c30]Nicholas Carlini:
How Private is Machine Learning? IH&MMSec 2021: 3 - [c29]Nicholas Carlini, Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Abhradeep Thakurta, Florian Tramèr:
Is Private Learning Possible with Instance Encoding? IEEE Symposium on Security and Privacy 2021: 410-427 - [c28]Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Nicholas Carlini:
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning. IEEE Symposium on Security and Privacy 2021: 866-882 - [c27]Nicholas Carlini:
Poisoning the Unlabeled Dataset of Semi-Supervised Learning. USENIX Security Symposium 2021: 1577-1592 - [c26]Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B. Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, Colin Raffel:
Extracting Training Data from Large Language Models. USENIX Security Symposium 2021: 2633-2650 - [e2]Nicholas Carlini, Ambra Demontis, Yizheng Chen:
AISec@CCS 2021: Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security, Virtual Event, Republic of Korea, 15 November 2021. ACM 2021, ISBN 978-1-4503-8657-9 [contents] - [i46]Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Nicholas Carlini:
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning. CoRR abs/2101.04535 (2021) - [i45]Nicholas Carlini:
Poisoning the Unlabeled Dataset of Semi-Supervised Learning. CoRR abs/2105.01622 (2021) - [i44]Sanghyun Hong, Nicholas Carlini, Alexey Kurakin:
Handcrafted Backdoors in Deep Neural Networks. CoRR abs/2106.04690 (2021) - [i43]David Berthelot, Rebecca Roelofs, Kihyuk Sohn, Nicholas Carlini, Alex Kurakin:
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation. CoRR abs/2106.04732 (2021) - [i42]Nicholas Carlini, Andreas Terzis:
Poisoning and Backdooring Contrastive Learning. CoRR abs/2106.09667 (2021) - [i41]Maura Pintor, Luca Demetrio, Angelo Sotgiu, Giovanni Manca, Ambra Demontis, Nicholas Carlini, Battista Biggio, Fabio Roli:
Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples. CoRR abs/2106.09947 (2021) - [i40]Oliver Bryniarski, Nabeel Hingun, Pedro Pachuca, Vincent Wang, Nicholas Carlini:
Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent. CoRR abs/2106.15023 (2021) - [i39]Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, Nicholas Carlini:
Deduplicating Training Data Makes Language Models Better. CoRR abs/2107.06499 (2021) - [i38]Nicholas Carlini, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Florian Tramèr:
NeuraCrypt is not private. CoRR abs/2108.07256 (2021) - [i37]Dan Hendrycks, Nicholas Carlini, John Schulman, Jacob Steinhardt:
Unsolved Problems in ML Safety. CoRR abs/2109.13916 (2021) - [i36]Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, Florian Tramèr:
Membership Inference Attacks From First Principles. CoRR abs/2112.03570 (2021) - [i35]Chiyuan Zhang, Daphne Ippolito, Katherine Lee, Matthew Jagielski, Florian Tramèr, Nicholas Carlini:
Counterfactual Memorization in Neural Language Models. CoRR abs/2112.12938 (2021) - 2020
- [c25]Sadia Afroz, Nicholas Carlini, Ambra Demontis:
AISec'20: 13th Workshop on Artificial Intelligence and Security. CCS 2020: 2143-2144 - [c24]Nicholas Carlini, Matthew Jagielski, Ilya Mironov:
Cryptanalytic Extraction of Neural Network Models. CRYPTO (3) 2020: 189-218 - [c23]Nicholas Carlini, Hany Farid:
Evading Deepfake-Image Detectors with White- and Black-Box Attacks. CVPR Workshops 2020: 2804-2813 - [c22]David Berthelot, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, Colin Raffel:
ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring. ICLR 2020 - [c21]Florian Tramèr, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Jörn-Henrik Jacobsen:
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations. ICML 2020: 9561-9571 - [c20]Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin Raffel, Ekin Dogus Cubuk, Alexey Kurakin, Chun-Liang Li:
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence. NeurIPS 2020 - [c19]Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, Ludwig Schmidt:
Measuring Robustness to Natural Distribution Shifts in Image Classification. NeurIPS 2020 - [c18]Florian Tramèr, Nicholas Carlini, Wieland Brendel, Aleksander Madry:
On Adaptive Attacks to Adversarial Example Defenses. NeurIPS 2020 - [c17]Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, Nicolas Papernot:
High Accuracy and High Fidelity Extraction of Neural Networks. USENIX Security Symposium 2020: 1345-1362 - [i34]Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Han Zhang, Colin Raffel:
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence. CoRR abs/2001.07685 (2020) - [i33]Florian Tramèr, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Jörn-Henrik Jacobsen:
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations. CoRR abs/2002.04599 (2020) - [i32]Florian Tramèr, Nicholas Carlini, Wieland Brendel, Aleksander Madry:
On Adaptive Attacks to Adversarial Example Defenses. CoRR abs/2002.08347 (2020) - [i31]Nicholas Carlini, Matthew Jagielski, Ilya Mironov:
Cryptanalytic Extraction of Neural Network Models. CoRR abs/2003.04884 (2020) - [i30]Nicholas Carlini, Hany Farid:
Evading Deepfake-Image Detectors with White- and Black-Box Attacks. CoRR abs/2004.00622 (2020) - [i29]Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, Ludwig Schmidt:
Measuring Robustness to Natural Distribution Shifts in Image Classification. CoRR abs/2007.00644 (2020) - [i28]Christopher A. Choquette-Choo, Florian Tramèr, Nicholas Carlini, Nicolas Papernot:
Label-Only Membership Inference Attacks. CoRR abs/2007.14321 (2020) - [i27]Nicholas Carlini:
A Partial Break of the Honeypots Defense to Catch Adversarial Attacks. CoRR abs/2009.10975 (2020) - [i26]Guneet S. Dhillon, Nicholas Carlini:
Erratum Concerning the Obfuscated Gradients Attack on Stochastic Activation Pruning. CoRR abs/2010.00071 (2020) - [i25]Nicholas Carlini, Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Shuang Song, Abhradeep Thakurta, Florian Tramèr:
An Attack on InstaHide: Is Private Learning Possible with Instance Encoding? CoRR abs/2011.05315 (2020) - [i24]Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B. Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, Colin Raffel:
Extracting Training Data from Large Language Models. CoRR abs/2012.07805 (2020)
2010 – 2019
- 2019
- [c16]Sadia Afroz, Battista Biggio, Nicholas Carlini, Yuval Elovici, Asaf Shabtai:
AISec'19: 12th ACM Workshop on Artificial Intelligence and Security. CCS 2019: 2707-2708 - [c15]Justin Gilmer, Nicolas Ford, Nicholas Carlini, Ekin D. Cubuk:
Adversarial Examples Are a Natural Consequence of Test Error in Noise. ICML 2019: 2280-2289 - [c14]Yao Qin, Nicholas Carlini, Garrison W. Cottrell, Ian J. Goodfellow, Colin Raffel:
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition. ICML 2019: 5231-5240 - [c13]David Berthelot, Nicholas Carlini, Ian J. Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel:
MixMatch: A Holistic Approach to Semi-Supervised Learning. NeurIPS 2019: 5050-5060 - [c12]Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, Dawn Song:
The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks. USENIX Security Symposium 2019: 267-284 - [e1]Lorenzo Cavallaro, Johannes Kinder, Sadia Afroz, Battista Biggio, Nicholas Carlini, Yuval Elovici, Asaf Shabtai:
Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security, AISec@CCS 2019, London, UK, November 15, 2019. ACM 2019, ISBN 978-1-4503-6833-9 [contents] - [i23]Nic Ford, Justin Gilmer, Nicholas Carlini, Ekin Dogus Cubuk:
Adversarial Examples Are a Natural Consequence of Test Error in Noise. CoRR abs/1901.10513 (2019) - [i22]Nicholas Carlini:
Is AmI (Attacks Meet Interpretability) Robust to Adversarial Examples? CoRR abs/1902.02322 (2019) - [i21]Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian J. Goodfellow, Aleksander Madry, Alexey Kurakin:
On Evaluating Adversarial Robustness. CoRR abs/1902.06705 (2019) - [i20]Yao Qin, Nicholas Carlini, Ian J. Goodfellow, Garrison W. Cottrell, Colin Raffel:
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition. CoRR abs/1903.10346 (2019) - [i19]Jörn-Henrik Jacobsen, Jens Behrmann, Nicholas Carlini, Florian Tramèr, Nicolas Papernot:
Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness. CoRR abs/1903.10484 (2019) - [i18]Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. 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 M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li
, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan R. Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar:
SysML: The New Frontier of Machine Learning Systems. CoRR abs/1904.03257 (2019) - [i17]David Berthelot, Nicholas Carlini, Ian J. Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel:
MixMatch: A Holistic Approach to Semi-Supervised Learning. CoRR abs/1905.02249 (2019) - [i16]Nicholas Carlini:
A critique of the DeepSec Platform for Security Analysis of Deep Learning Models. CoRR abs/1905.07112 (2019) - [i15]Steven Chen, Nicholas Carlini, David A. Wagner:
Stateful Detection of Black-Box Adversarial Attacks. CoRR abs/1907.05587 (2019) - [i14]Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, Nicolas Papernot:
High-Fidelity Extraction of Neural Network Models. CoRR abs/1909.01838 (2019) - [i13]Nicholas Carlini, Úlfar Erlingsson, Nicolas Papernot:
Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applications. CoRR abs/1910.13427 (2019) - [i12]David Berthelot, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, Colin Raffel:
ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring. CoRR abs/1911.09785 (2019) - 2018
- [b1]Nicholas Carlini:
Evaluation and Design of Robust Neural Network Defenses. University of California, Berkeley, USA, 2018 - [c11]Anish Athalye, Nicholas Carlini, David A. Wagner:
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples. ICML 2018: 274-283 - [c10]Nicholas Carlini, David A. Wagner:
Audio Adversarial Examples: Targeted Attacks on Speech-to-Text. IEEE Symposium on Security and Privacy Workshops 2018: 1-7 - [i11]Nicholas Carlini, David A. Wagner:
Audio Adversarial Examples: Targeted Attacks on Speech-to-Text. CoRR abs/1801.01944 (2018) - [i10]Anish Athalye, Nicholas Carlini, David A. Wagner:
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples. CoRR abs/1802.00420 (2018) - [i9]Nicholas Carlini, Chang Liu, Jernej Kos, Úlfar Erlingsson, Dawn Song:
The Secret Sharer: Measuring Unintended Neural Network Memorization & Extracting Secrets. CoRR abs/1802.08232 (2018) - [i8]Anish Athalye, Nicholas Carlini:
On the Robustness of the CVPR 2018 White-Box Adversarial Example Defenses. CoRR abs/1804.03286 (2018) - [i7]Tom B. Brown, Nicholas Carlini, Chiyuan Zhang, Catherine Olsson, Paul F. Christiano, Ian J. Goodfellow:
Unrestricted Adversarial Examples. CoRR abs/1809.08352 (2018) - 2017
- [c9]Nicholas Carlini, David A. Wagner:
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods. AISec@CCS 2017: 3-14 - [c8]Nicholas Carlini, David A. Wagner:
Towards Evaluating the Robustness of Neural Networks. IEEE Symposium on Security and Privacy 2017: 39-57 - [c7]Warren He, James Wei, Xinyun Chen, Nicholas Carlini, Dawn Song:
Adversarial Example Defense: Ensembles of Weak Defenses are not Strong. WOOT 2017 - [i6]Nicholas Carlini, David A. Wagner:
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods. CoRR abs/1705.07263 (2017) - [i5]Warren He, James Wei, Xinyun Chen, Nicholas Carlini, Dawn Song:
Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong. CoRR abs/1706.04701 (2017) - [i4]Nicholas Carlini, Guy Katz, Clark W. Barrett, David L. Dill:
Ground-Truth Adversarial Examples. CoRR abs/1709.10207 (2017) - [i3]Nicholas Carlini, David A. Wagner:
MagNet and "Efficient Defenses Against Adversarial Attacks" are Not Robust to Adversarial Examples. CoRR abs/1711.08478 (2017) - 2016
- [c6]Nicholas Carlini, Pratyush Mishra, Tavish Vaidya, Yuankai Zhang, Micah Sherr, Clay Shields, David A. Wagner, Wenchao Zhou:
Hidden Voice Commands. USENIX Security Symposium 2016: 513-530 - [i2]Nicholas Carlini, David A. Wagner:
Defensive Distillation is Not Robust to Adversarial Examples. CoRR abs/1607.04311 (2016) - [i1]Nicholas Carlini, David A. Wagner:
Towards Evaluating the Robustness of Neural Networks. CoRR abs/1608.04644 (2016) - 2015
- [c5]Nicholas Carlini, Antonio Barresi, Mathias Payer, David A. Wagner, Thomas R. Gross:
Control-Flow Bending: On the Effectiveness of Control-Flow Integrity. USENIX Security Symposium 2015: 161-176 - 2014
- [c4]Nicholas Carlini, David A. Wagner:
ROP is Still Dangerous: Breaking Modern Defenses. USENIX Security Symposium 2014: 385-399 - 2013
- [c3]Eric Kim, Nicholas Carlini, Andrew Chang, George Yiu, Kai Wang, David A. Wagner:
Improved Support for Machine-assisted Ballot-level Audits. EVT/WOTE 2013 - 2012
- [c2]Nicholas Carlini, Adrienne Porter Felt, David A. Wagner:
An Evaluation of the Google Chrome Extension Security Architecture. USENIX Security Symposium 2012: 97-111 - [c1]Kai Wang, Nicholas Carlini, Eric Kim, Ivan Motyashov, Daniel Nguyen, David A. Wagner:
Operator-Assisted Tabulation of Optical Scan Ballots. EVT/WOTE 2012
Coauthor Index
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