World's Best Scientists 2026 revealed!
Nicolas Papernot

Nicolas Papernot

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Rising Stars
2025

D-Index & Metrics

Rising Stars

D-Index
53
Citations
30133
World Ranking
239
National Ranking
10

Computer Science

D-Index
54
Citations
31933
World Ranking
4423
National Ranking
176

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Nicolas Papernot is affiliated with the University of Toronto in Canada and has contributed extensively to the field of computer science with a focus on artificial intelligence and privacy-preserving technologies. Their work spans several subfields, including computer vision and pattern recognition, signal processing, hardware and architecture, and computer networks and communications.

Their research topics cover a variety of areas such as adversarial robustness in machine learning, privacy-preserving technologies in data, anomaly detection techniques and applications, advanced malware detection techniques, explainable artificial intelligence (XAI), stochastic gradient optimization techniques, and advanced neural network applications.

Among their recent publications are:

  • "AI models collapse when trained on recursively generated data," 2024, published in Nature
  • "Public code for Model Collapse," 2024, published on arXiv (Cornell University)
  • "Label-Only Membership Inference Attacks," 2020, published on arXiv (Cornell University)
  • "Tempered Sigmoid Activations for Deep Learning with Differential Privacy," 2021, published in the Proceedings of the AAAI Conference on Artificial Intelligence
  • "Bad Characters: Imperceptible NLP Attacks," 2022, published at the 2022 IEEE Symposium on Security and Privacy (SP)

Their frequent coauthors include Ilia Shumailov, Adam Dziedzic, Anvith Thudi, Christopher A. Choquette-Choo, and Franziska Boenisch.

They have published frequently in venues such as arXiv (Cornell University), Proceedings on Privacy Enhancing Technologies, Nature, Nature Communications, and the SSRN Electronic Journal.

Best Publications

  • The Limitations of Deep Learning in Adversarial Settings

    Nicolas Papernot;Patrick McDaniel;Somesh Jha;Matt Fredrikson

  • Practical Black-Box Attacks against Machine Learning

    Nicolas Papernot;Patrick McDaniel;Ian Goodfellow;Somesh Jha

  • Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks

    Nicolas Papernot;Patrick McDaniel;Xi Wu;Somesh Jha

  • Ensemble Adversarial Training: Attacks and Defenses

    Florian Tramèr;Alexey Kurakin;Nicolas Papernot;Ian J. Goodfellow

  • MixMatch: A Holistic Approach to Semi-Supervised Learning

    David Berthelot;Nicholas Carlini;Ian Goodfellow;Nicolas Papernot

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

    Nicolas Papernot;Patrick D. McDaniel;Ian J. Goodfellow

  • On Evaluating Adversarial Robustness

    Nicholas Carlini;Anish Athalye;Nicolas Papernot;Wieland Brendel

  • Towards the Science of Security and Privacy in Machine Learning

    Nicolas Papernot;Patrick D. McDaniel;Arunesh Sinha;Michael P. Wellman

  • On the (Statistical) Detection of Adversarial Examples

    Kathrin Grosse;Praveen Manoharan;Nicolas Papernot;Michael Backes

  • Adversarial Attacks on Neural Network Policies

    Sandy H. Huang;Nicolas Papernot;Ian J. Goodfellow;Yan Duan

  • Technical Report on the CleverHans v2.1.0 Adversarial Examples Library

    Nicolas Papernot;Fartash Faghri;Nicholas Carlini;Ian Goodfellow

  • The Space of Transferable Adversarial Examples

    Florian Tramèr;Nicolas Papernot;Ian J. Goodfellow;Dan Boneh

  • Adversarial examples for malware detection

    Kathrin Grosse;Nicolas Papernot;Praveen Manoharan;Michael Backes

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

    Nicolas Papernot;Martín Abadi;Úlfar Erlingsson;Ian J. Goodfellow

  • Machine Unlearning

    Lucas Bourtoule;Varun Chandrasekaran;Christopher A. Choquette-Choo;Hengrui Jia

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

    Nicolas Papernot;Patrick D. McDaniel;Ian J. Goodfellow;Somesh Jha

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

    Nicolas Papernot;Patrick D. McDaniel

  • AI models collapse when trained on recursively generated data

    Unknown

  • Crafting adversarial input sequences for recurrent neural networks

    Nicolas Papernot;Patrick McDaniel;Ananthram Swami;Richard Harang

  • Adversarial Perturbations Against Deep Neural Networks for Malware Classification

    Kathrin Grosse;Nicolas Papernot;Praveen Manoharan;Michael Backes

  • SoK: Security and Privacy in Machine Learning

    Nicolas Papernot;Patrick McDaniel;Arunesh Sinha;Michael P. Wellman

Frequent Co-Authors

Patrick McDaniel
Patrick McDaniel University of Wisconsin–Madison
Ian Goodfellow
Ian Goodfellow Google (United States)
Nicholas Carlini
Nicholas Carlini Google (United States)
Ananthram Swami
Ananthram Swami United States Army Research Laboratory
Somesh Jha
Somesh Jha University of Wisconsin–Madison
Ross Anderson
Ross Anderson University of Cambridge
Dan Boneh
Dan Boneh Stanford University
Kunal Talwar
Kunal Talwar Apple (United States)

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