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Nicolas Papernot

Active 2014–2026

164
Papers
30,612
Citations
52
h-index
104
i10-index

Citations

Citations per year for Nicolas Papernot1989: 1 citations1995: 1 citations2000: 1 citations2006: 2 citations2007: 1 citations2009: 3 citations2010: 1 citations2014: 1 citations2015: 2 citations2016: 64 citations2017: 367 citations2018: 1,190 citations2019: 1,788 citations2020: 2,411 citations2021: 2,590 citations2022: 1,926 citations2023: 2,011 citations2024: 1,830 citations2025: 1,807 citations2026: 586 citations2027: 2 citations1990–1994: no citations, so these years are not shown1996–1999: no citations, so these years are not shown2001–2005: no citations, so these years are not shown2008: no citations, so this year is not shown2011–2013: no citations, so these years are not shown

Citation sources

Countries

World map of the countries and regions citing this authorUnited States: 3,358 citing papers, 26.7% of this breakdownChina: 2,601 citing papers, 20.6% of this breakdownUnited Kingdom: 721 citing papers, 5.7% of this breakdownGermany: 534 citing papers, 4.2% of this breakdownAustralia: 488 citing papers, 3.9% of this breakdownCanada: 463 citing papers, 3.7% of this breakdownSingapore: 317 citing papers, 2.5% of this breakdownIndia: 316 citing papers, 2.5% of this breakdownItaly: 316 citing papers, 2.5% of this breakdownSouth Korea: 303 citing papers, 2.4% of this breakdownJapan: 257 citing papers, 2% of this breakdownFrance: 253 citing papers, 2% of this breakdown
0%26.7%Other 21.3%

Institutions

Fields

  • Computer Science87.6%
  • Engineering3.4%
  • Social Sciences2.4%
  • Medicine1.6%
  • Decision Sciences0.9%
  • Biochemistry, Genetics and Molecular Biology0.8%
  • Other3.3%

Topics

  • Adversarial Robustness in Machine Learning20.7%
  • Anomaly Detection Techniques and Applications8.5%
  • Advanced Malware Detection Techniques6.4%
  • Privacy-Preserving Technologies in Data4.6%
  • Advanced Neural Network Applications3.6%
  • Network Security and Intrusion Detection3.2%
  • Other53%

Coauthors

All papers

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  1. The Limitations of Deep Learning in Adversarial Settings

    Authors: Nicolas Papernot, Patrick D. McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, Ananthram Swami - IEEE European Symposium on Security and Privacy (EuroS&P), EuroS&P 2016 cited by 3,920

  2. AI models collapse when trained on recursively generated data

    Authors: Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Nicolas Papernot, Ross J. Anderson, Yarin Gal - Nature, Nat. 2024 cited by 677

  3. The Curse of Recursion: Training on Generated Data Makes Models Forget

    Authors: Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Yarin Gal, Nicolas Papernot, Ross J. Anderson - arXiv (Cornell University), CoRR 2023 cited by 363

  4. Ensemble Adversarial Training: Attacks and Defenses

    Authors: Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, Patrick D. McDaniel - ICLR (Poster) 2018 cited by 3,060

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

    Authors: Nicolas Papernot, Patrick D. McDaniel, Ian J. Goodfellow - arXiv (Cornell University), CoRR 2016 cited by 1,425

  6. On Evaluating Adversarial Robustness

    Authors: Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian J. Goodfellow, Aleksander Madry, Alexey Kurakin - arXiv (Cornell University), CoRR 2019 cited by 747

  7. SoK: Security and Privacy in Machine Learning

    Authors: Nicolas Papernot, Patrick D. McDaniel, Arunesh Sinha, Michael P. Wellman - IEEE European Symposium on Security and Privacy (EuroS&P), EuroS&P 2018 cited by 405

  8. MixMatch: A Holistic Approach to Semi-Supervised Learning

    Authors: David Berthelot, Nicholas Carlini, Ian J. Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel - NeurIPS 2019 cited by 3,596

  9. Adversarial Attacks on Neural Network Policies

    Authors: Sandy H. Huang, Nicolas Papernot, Ian J. Goodfellow, Yan Duan, Pieter Abbeel - ICLR (Workshop) 2017 cited by 957

  10. Unrolling SGD: Understanding Factors Influencing Machine Unlearning

    Authors: Anvith Thudi, Gabriel Deza, Varun Chandrasekaran, Nicolas Papernot - IEEE 7th European Symposium on Security and Privacy (EuroS&P), EuroS&P 2022 cited by 114

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

    Authors: Nicolas Papernot, Patrick D. McDaniel - arXiv (Cornell University), CoRR 2018 cited by 404

  12. The Space of Transferable Adversarial Examples

    Authors: Florian Tramèr, Nicolas Papernot, Ian J. Goodfellow, Dan Boneh, Patrick D. McDaniel - arXiv (Cornell University), CoRR 2017 cited by 455

  13. Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data

    Authors: Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, Kunal Talwar - ICLR 2017 cited by 1,157

  14. Adversarial Examples for Malware Detection

    Authors: Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, Patrick D. McDaniel - Lecture notes in computer science, ESORICS (2) 2017 cited by 553

  15. Machine Unlearning

    Authors: Lucas Bourtoule, Varun Chandrasekaran, Christopher A. Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, Nicolas Papernot - IEEE Symposium on Security and Privacy (SP) 2021 cited by 598

  16. Scalable Private Learning with PATE

    Authors: Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Úlfar Erlingsson - ICLR 2018 cited by 725

  17. On the (Statistical) Detection of Adversarial Examples

    Authors: Kathrin Grosse, Praveen Manoharan, Nicolas Papernot, Michael Backes, Patrick D. McDaniel - arXiv (Cornell University), CoRR 2017 cited by 457

  18. Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples

    Authors: Nicolas Papernot, Patrick D. McDaniel, Ian J. Goodfellow, Somesh Jha, Z. Berkay Celik, Ananthram Swami - on Asia Conference on Computer and Communications Security, AsiaCCS 2017 cited by 3,484

  19. cleverhans v0.1: an adversarial machine learning library

    Authors: Nicolas Papernot, Fartash Faghri, Nicholas Carlini, Ian Goodfellow, Reuben Feinman, Alexey Kurakin, Cihang Xie, Yash Sharma, T. B. 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 - arXiv (Cornell University), CoRR 2016 cited by 488

  20. LLM Censorship: A Machine Learning Challenge or a Computer Security Problem?

    Authors: David Glukhov, Ilia Shumailov, Yarin Gal, Nicolas Papernot, Vardan Papyan - arXiv (Cornell University), CoRR 2023 cited by 58

  21. UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI

    Authors: Ilia Shumailov, Jamie Hayes, Eleni Triantafillou, Guillermo Ortiz-Jiménez, Nicolas Papernot, Matthew Jagielski, Itay Yona, Heidi Howard, Eugene Bagdasaryan - arXiv (Cornell University), CoRR 2024 cited by 52

  22. Tempered Sigmoid Activations for Deep Learning with Differential Privacy

    Authors: Nicolas Papernot, Abhradeep Thakurta, Shuang Song, Steve Chien, Úlfar Erlingsson - AAAI Conference on Artificial Intelligence 2021 cited by 109

  23. Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy, Research, and Practice

    Authors: A. Feder Cooper, Christopher A. Choquette-Choo, Miranda Bogen, Kevin Klyman, Matthew Jagielski, Katja Filippova, Ken Liu, Alexandra Chouldechova, Jamie Hayes, Yangsibo Huang, Eleni Triantafillou, Peter Kairouz, Nicole Mitchell, Niloofar Mireshghallah, Abigail Z. Jacobs, James Grimmelmann, Vitaly Shmatikov, Christopher De Sa, Ilia Shumailov, Andreas Terzis, Solon Barocas, Jennifer Wortman Vaughan, danah boyd, Yejin Choi, Sanmi Koyejo, Fernando A. Delgado, Percy Liang, Daniel E. Ho, Pamela Samuelson, Miles Brundage, David Bau, Seth Neel, Hanna M. Wallach, Amy Cyphert, Mark A. Lemley, Nicolas Papernot, Katherine Lee - NeurIPS 2025 cited by 47

  24. On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping

    Authors: Sanghyun Hong, Varun Chandrasekaran, Yigitcan Kaya, Tudor Dumitras, Nicolas Papernot - arXiv (Cornell University), CoRR 2020 cited by 111