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ORCIDNicholas Carlini
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2020 – today
- 2024
[c62]Nicholas Carlini, Daniel Paleka, Krishnamurthy Dj Dvijotham, Thomas Steinke, Jonathan Hayase, A. Feder Cooper, Katherine Lee, Matthew Jagielski, Milad Nasr, Arthur Conmy, Eric Wallace, David Rolnick, Florian Tramèr:
Stealing part of a production language model. ICML 2024- [c61]Florian Tramèr, Gautam Kamath, Nicholas Carlini:
Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining. ICML 2024 - [c60]Edoardo Debenedetti, Nicholas Carlini, Florian Tramèr:
Evading Black-box Classifiers Without Breaking Eggs. SaTML 2024: 408-424 - [c59]Edoardo Debenedetti, Giorgio Severi, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski, Eric Wallace, Nicholas Carlini, Florian Tramèr:
Privacy Side Channels in Machine Learning Systems. USENIX Security Symposium 2024 - [i83]Jonathan Hayase, Ema Borevkovic, Nicholas Carlini, Florian Tramèr, Milad Nasr:
Query-Based Adversarial Prompt Generation. CoRR abs/2402.12329 (2024) - [i82]Nicholas Carlini, Daniel Paleka, Krishnamurthy (Dj) Dvijotham, Thomas Steinke, Jonathan Hayase, A. Feder Cooper, Katherine Lee, Matthew Jagielski, Milad Nasr, Arthur Conmy, Eric Wallace, David Rolnick, Florian Tramèr:
Stealing Part of a Production Language Model. CoRR abs/2403.06634 (2024) - [i81]Sanghyun Hong, Nicholas Carlini, Alexey Kurakin:
Diffusion Denoising as a Certified Defense against Clean-label Poisoning. CoRR abs/2403.11981 (2024) - [i80]Yuxin Wen, Leo Marchyok, Sanghyun Hong, Jonas Geiping, Tom Goldstein, Nicholas Carlini:
Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models. CoRR abs/2404.01231 (2024) - [i79]Yiming Zhang, Avi Schwarzschild, Nicholas Carlini, Zico Kolter, Daphne Ippolito:
Forcing Diffuse Distributions out of Language Models. CoRR abs/2404.10859 (2024) - [i78]Nicholas Carlini:
Cutting through buggy adversarial example defenses: fixing 1 line of code breaks Sabre. CoRR abs/2405.03672 (2024) - [i77]Robert Hönig, Javier Rando, Nicholas Carlini, Florian Tramèr:
Adversarial Perturbations Cannot Reliably Protect Artists From Generative AI. CoRR abs/2406.12027 (2024) - 2023
- [j1]Clark W. Barrett, Brad Boyd, Elie Bursztein, Nicholas Carlini, Brad Chen, Jihye Choi, Amrita Roy Chowdhury, Mihai Christodorescu, Anupam Datta, Soheil Feizi, Kathleen Fisher, Tatsunori Hashimoto, Dan Hendrycks, Somesh Jha, Daniel Kang
, Florian Kerschbaum, Eric Mitchell, John C. Mitchell, Zulfikar Ramzan, Khawaja Shams, Dawn Song, Ankur Taly, Diyi Yang:
Identifying and Mitigating the Security Risks of Generative AI. Found. Trends Priv. Secur. 6(1): 1-52 (2023) - [c58]Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramèr, Chiyuan Zhang:
Quantifying Memorization Across Neural Language Models. ICLR 2023 - [c57]Nicholas Carlini, Florian Tramèr, Krishnamurthy (Dj) Dvijotham, Leslie Rice, Mingjie Sun, J. Zico Kolter:
(Certified!!) Adversarial Robustness for Free! ICLR 2023 - [c56]Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Guha Thakurta, Nicolas Papernot, Chiyuan Zhang:
Measuring Forgetting of Memorized Training Examples. ICLR 2023 - [c55]Chawin Sitawarin, Kornrapat Pongmala, Yizheng Chen, Nicholas Carlini, David A. Wagner:
Part-Based Models Improve Adversarial Robustness. ICLR 2023 - [c54]Chawin Sitawarin, Florian Tramèr, Nicholas Carlini:
Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems. ICML 2023: 32008-32032 - [c53]Daphne Ippolito, Florian Tramèr, Milad Nasr, Chiyuan Zhang, Matthew Jagielski, Katherine Lee, Christopher A. Choquette-Choo, Nicholas Carlini:
Preventing Generation of Verbatim Memorization in Language Models Gives a False Sense of Privacy. INLG 2023: 28-53 - [c52]Daphne Ippolito, Nicholas Carlini, Katherine Lee, Milad Nasr, Yun William Yu:
Reverse-Engineering Decoding Strategies Given Blackbox Access to a Language Generation System. INLG 2023: 396-406 - [c51]Nicholas Carlini, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski, Irena Gao, Pang Wei Koh, Daphne Ippolito, Florian Tramèr, Ludwig Schmidt:
Are aligned neural networks adversarially aligned? NeurIPS 2023 - [c50]Matthew Jagielski, Milad Nasr, Katherine Lee, Christopher A. Choquette-Choo, Nicholas Carlini, Florian Tramèr:
Students Parrot Their Teachers: Membership Inference on Model Distillation. NeurIPS 2023 - [c49]Zhouxing Shi, Nicholas Carlini, Ananth Balashankar, Ludwig Schmidt, Cho-Jui Hsieh, Alex Beutel, Yao Qin:
Effective Robustness against Natural Distribution Shifts for Models with Different Training Data. NeurIPS 2023 - [c48]Chiyuan Zhang, Daphne Ippolito, Katherine Lee, Matthew Jagielski, Florian Tramèr, Nicholas Carlini:
Counterfactual Memorization in Neural Language Models. NeurIPS 2023 - [c47]Sanghyun Hong, Nicholas Carlini, Alexey Kurakin:
Publishing Efficient On-device Models Increases Adversarial Vulnerability. SaTML 2023: 271-290 - [c46]Milad Nasr, Jamie Hayes, Thomas Steinke, Borja Balle, Florian Tramèr, Matthew Jagielski, Nicholas Carlini, Andreas Terzis:
Tight Auditing of Differentially Private Machine Learning. USENIX Security Symposium 2023: 1631-1648 - [c45]Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, Eric Wallace:
Extracting Training Data from Diffusion Models. USENIX Security Symposium 2023: 5253-5270 - [i76]Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne Ippolito, Eric Wallace:
Extracting Training Data from Diffusion Models. CoRR abs/2301.13188 (2023) - [i75]Zhouxing Shi, Nicholas Carlini, Ananth Balashankar, Ludwig Schmidt, Cho-Jui Hsieh, Alex Beutel, Yao Qin:
Effective Robustness against Natural Distribution Shifts for Models with Different Training Data. CoRR abs/2302.01381 (2023) - [i74]Milad Nasr, Jamie Hayes, Thomas Steinke, Borja Balle, Florian Tramèr, Matthew Jagielski, Nicholas Carlini, Andreas Terzis:
Tight Auditing of Differentially Private Machine Learning. CoRR abs/2302.07956 (2023) - [i73]Nicholas Carlini, Matthew Jagielski, Christopher A. Choquette-Choo, Daniel Paleka, Will Pearce, Hyrum S. Anderson, Andreas Terzis, Kurt Thomas, Florian Tramèr:
Poisoning Web-Scale Training Datasets is Practical. CoRR abs/2302.10149 (2023) - [i72]Keane Lucas, Matthew Jagielski, Florian Tramèr, Lujo Bauer, Nicholas Carlini:
Randomness in ML Defenses Helps Persistent Attackers and Hinders Evaluators. CoRR abs/2302.13464 (2023) - [i71]Matthew Jagielski, Milad Nasr, Christopher A. Choquette-Choo, Katherine Lee, Nicholas Carlini:
Students Parrot Their Teachers: Membership Inference on Model Distillation. CoRR abs/2303.03446 (2023) - [i70]Edoardo Debenedetti, Nicholas Carlini, Florian Tramèr:
Evading Black-box Classifiers Without Breaking Eggs. CoRR abs/2306.02895 (2023) - [i69]Nicholas Carlini, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski, Irena Gao, Anas Awadalla, Pang Wei Koh, Daphne Ippolito, Katherine Lee, Florian Tramèr, Ludwig Schmidt:
Are aligned neural networks adversarially aligned? CoRR abs/2306.15447 (2023) - [i68]Nikhil Kandpal, Matthew Jagielski, Florian Tramèr, Nicholas Carlini:
Backdoor Attacks for In-Context Learning with Language Models. CoRR abs/2307.14692 (2023) - [i67]Nicholas Carlini:
A LLM Assisted Exploitation of AI-Guardian. CoRR abs/2307.15008 (2023) - [i66]Clark W. Barrett, Brad Boyd, Ellie Burzstein, Nicholas Carlini, Brad Chen, Jihye Choi, Amrita Roy Chowdhury, Mihai Christodorescu, Anupam Datta, Soheil Feizi, Kathleen Fisher, Tatsunori Hashimoto, Dan Hendrycks, Somesh Jha, Daniel Kang, Florian Kerschbaum, Eric Mitchell, John C. Mitchell, Zulfikar Ramzan, Khawaja Shams, Dawn Song, Ankur Taly, Diyi Yang:
Identifying and Mitigating the Security Risks of Generative AI. CoRR abs/2308.14840 (2023) - [i65]Daphne Ippolito, Nicholas Carlini, Katherine Lee, Milad Nasr, Yun William Yu:
Reverse-Engineering Decoding Strategies Given Blackbox Access to a Language Generation System. CoRR abs/2309.04858 (2023) - [i64]Edoardo Debenedetti, Giorgio Severi, Nicholas Carlini, Christopher A. Choquette-Choo, Matthew Jagielski, Milad Nasr, Eric Wallace, Florian Tramèr:
Privacy Side Channels in Machine Learning Systems. CoRR abs/2309.05610 (2023) - [i63]A. Feder Cooper, Katherine Lee, James Grimmelmann, Daphne Ippolito, Christopher Callison-Burch, Christopher A. Choquette-Choo, Niloofar Mireshghallah, Miles Brundage, David Mimno, Madiha Zahrah Choksi, Jack M. Balkin, Nicholas Carlini, Christopher De Sa, Jonathan Frankle, Deep Ganguli, Bryant Gipson, Andres Guadamuz, Swee Leng Harris, Abigail Z. Jacobs, Elizabeth Joh, Gautam Kamath, Mark Lemley, Cass Matthews, Christine McLeavey, Corynne McSherry, Milad Nasr, Paul Ohm, Adam Roberts, Tom Rubin, Pamela Samuelson, Ludwig Schubert, Kristen Vaccaro, Luis Villa, Felix Wu, Elana Zeide:
Report of the 1st Workshop on Generative AI and Law. CoRR abs/2311.06477 (2023) - [i62]Milad Nasr, Nicholas Carlini, Jonathan Hayase, Matthew Jagielski, A. Feder Cooper, Daphne Ippolito, Christopher A. Choquette-Choo, Eric Wallace, Florian Tramèr, Katherine Lee:
Scalable Extraction of Training Data from (Production) Language Models. CoRR abs/2311.17035 (2023) - [i61]Andong Hua, Jindong Gu, Zhiyu Xue, Nicholas Carlini, Eric Wong, Yao Qin:
Initialization Matters for Adversarial Transfer Learning. CoRR abs/2312.05716 (2023) - 2022
- [c44]Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, Nicholas Carlini:
Deduplicating Training Data Makes Language Models Better. ACL (1) 2022: 8424-8445 - [c43]Florian Tramèr, Reza Shokri, Ayrton San Joaquin, Hoang Le, Matthew Jagielski, Sanghyun Hong, Nicholas Carlini:
Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets. CCS 2022: 2779-2792 - [c42]David Berthelot, Rebecca Roelofs, Kihyuk Sohn, Nicholas Carlini, Alexey Kurakin:
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation. ICLR 2022 - [c41]Oliver Bryniarski, Nabeel Hingun, Pedro Pachuca, Vincent Wang, Nicholas Carlini:
Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent. ICLR 2022 - [c40]Nicholas Carlini, Andreas Terzis:
Poisoning and Backdooring Contrastive Learning. ICLR 2022 - [c39]Evani Radiya-Dixit, Sanghyun Hong, Nicholas Carlini, Florian Tramèr:
Data Poisoning Won't Save You From Facial Recognition. ICLR 2022 - [c38]Sanghyun Hong, Nicholas Carlini, Alexey Kurakin:
Handcrafted Backdoors in Deep Neural Networks. NeurIPS 2022 - [c37]Nicholas Carlini, Matthew Jagielski, Chiyuan Zhang, Nicolas Papernot, Andreas Terzis, Florian Tramèr:
The Privacy Onion Effect: Memorization is Relative. NeurIPS 2022 - [c36]Maura Pintor, Luca Demetrio, Angelo Sotgiu, Ambra Demontis, Nicholas Carlini, Battista Biggio, Fabio Roli:
Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples. NeurIPS 2022 - [c35]Roland S. Zimmermann, Wieland Brendel, Florian Tramèr, Nicholas Carlini:
Increasing Confidence in Adversarial Robustness Evaluations. NeurIPS 2022 - [c34]Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, Florian Tramèr:
Membership Inference Attacks From First Principles. SP 2022: 1897-1914 - [i60]Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramèr, Chiyuan Zhang:
Quantifying Memorization Across Neural Language Models. CoRR abs/2202.07646 (2022) - [i59]Florian Tramèr, Andreas Terzis, Thomas Steinke, Shuang Song, Matthew Jagielski, Nicholas Carlini:
Debugging Differential Privacy: A Case Study for Privacy Auditing. CoRR abs/2202.12219 (2022) - [i58]Florian Tramèr, Reza Shokri, Ayrton San Joaquin, Hoang Le, Matthew Jagielski, Sanghyun Hong, Nicholas Carlini:
Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets. CoRR abs/2204.00032 (2022) - [i57]Nicholas Carlini, Matthew Jagielski, Chiyuan Zhang, Nicolas Papernot, Andreas Terzis, Florian Tramèr:
The Privacy Onion Effect: Memorization is Relative. CoRR abs/2206.10469 (2022) - [i56]Nicholas Carlini, Florian Tramèr, Krishnamurthy Dvijotham, J. Zico Kolter:
(Certified!!) Adversarial Robustness for Free! CoRR abs/2206.10550 (2022) - [i55]Roland S. Zimmermann, Wieland Brendel, Florian Tramèr, Nicholas Carlini:
Increasing Confidence in Adversarial Robustness Evaluations. CoRR abs/2206.13991 (2022) - [i54]Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Chiyuan Zhang:
Measuring Forgetting of Memorized Training Examples. CoRR abs/2207.00099 (2022) - [i53]Chawin Sitawarin, Kornrapat Pongmala, Yizheng Chen, Nicholas Carlini, David A. Wagner:
Part-Based Models Improve Adversarial Robustness. CoRR abs/2209.09117 (2022) - [i52]Nicholas Carlini, Vitaly Feldman, Milad Nasr:
No Free Lunch in "Privacy for Free: How does Dataset Condensation Help Privacy". CoRR abs/2209.14987 (2022) - [i51]Chawin Sitawarin, Florian Tramèr, Nicholas Carlini:
Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems. CoRR abs/2210.03297 (2022) - [i50]Daphne Ippolito, Florian Tramèr, Milad Nasr, Chiyuan Zhang, Matthew Jagielski, Katherine Lee, Christopher A. Choquette-Choo, Nicholas Carlini:
Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy. CoRR abs/2210.17546 (2022) - [i49]Florian Tramèr, Gautam Kamath, Nicholas Carlini:
Considerations for Differentially Private Learning with Large-Scale Public Pretraining. CoRR abs/2212.06470 (2022) - [i48]Sanghyun Hong, Nicholas Carlini, Alexey Kurakin:
Publishing Efficient On-device Models Increases Adversarial Vulnerability. CoRR abs/2212.13700 (2022) - [i47]Battista Biggio, Nicholas Carlini, Pavel Laskov, Konrad Rieck, Antonio Emanuele Cinà:
Security of Machine Learning (Dagstuhl Seminar 22281). Dagstuhl Reports 12(7): 41-61 (2022) - 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? SP 2021: 410-427 - [c28]Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Nicholas Carlini:
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning. SP 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]