2026 Nicolas Papernot: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Rising Stars	53	239	239	10	10	146	30133
Computer Science	54	4423	4298	175	165	146	31933

Nicolas Papernot publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Nicolas Papernot sits on this spectrum.

32 publications 991+

This scientist: 146 publications — 25th percentile

25% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Nicolas Papernot D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Nicolas Papernot sits on this spectrum.

30 D-Index 131+

This scientist: 54 D-Index — 69th percentile

69% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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

4286
Citations
Practical Black-Box Attacks against Machine Learning

Nicolas Papernot;Patrick McDaniel;Ian Goodfellow;Somesh Jha

3665
Citations
Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks

Nicolas Papernot;Patrick McDaniel;Xi Wu;Somesh Jha

3209
Citations
Ensemble Adversarial Training: Attacks and Defenses

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

2672
Citations
MixMatch: A Holistic Approach to Semi-Supervised Learning

David Berthelot;Nicholas Carlini;Ian Goodfellow;Nicolas Papernot

2185
Citations
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples

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

2009
Citations
On Evaluating Adversarial Robustness

Nicholas Carlini;Anish Athalye;Nicolas Papernot;Wieland Brendel

793
Citations
Towards the Science of Security and Privacy in Machine Learning

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

754
Citations
On the (Statistical) Detection of Adversarial Examples

Kathrin Grosse;Praveen Manoharan;Nicolas Papernot;Michael Backes

636
Citations
Adversarial Attacks on Neural Network Policies

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

624
Citations
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library

Nicolas Papernot;Fartash Faghri;Nicholas Carlini;Ian Goodfellow

597
Citations
The Space of Transferable Adversarial Examples

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

587
Citations
Adversarial examples for malware detection

Kathrin Grosse;Nicolas Papernot;Praveen Manoharan;Michael Backes

567
Citations
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data

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

548
Citations
Machine Unlearning

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

528
Citations
Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples.

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

510
Citations
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning

Nicolas Papernot;Patrick D. McDaniel

492
Citations
AI models collapse when trained on recursively generated data

Unknown

480
Citations
Crafting adversarial input sequences for recurrent neural networks

Nicolas Papernot;Patrick McDaniel;Ananthram Swami;Richard Harang

458
Citations
Adversarial Perturbations Against Deep Neural Networks for Malware Classification

Kathrin Grosse;Nicolas Papernot;Praveen Manoharan;Michael Backes

447
Citations
SoK: Security and Privacy in Machine Learning

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

292
Citations

Frequent Co-Authors

Patrick McDaniel University of Wisconsin–Madison

Ian Goodfellow Google (United States)

Nicholas Carlini Google (United States)

Ananthram Swami United States Army Research Laboratory

Ulfar Erlingsson Lacework

Somesh Jha University of Wisconsin–Madison

Florian Tramèr ETH Zurich

Ross Anderson University of Cambridge

Dan Boneh Stanford University

Kunal Talwar Apple (United States)

External Links

Personal website of Nicolas Papernot Google Scholar page

If you think any of the details on this page are incorrect, let us know.

Related Online Degrees & Career Pathways

Expanding your expertise in computer science doesn’t have to mean stepping away from work or relocating. Today, a variety of flexible online degrees offer pathways to in-demand tech careers and advanced opportunities.

For those interested in business and management within tech fields, investigating the cheapest online mba programs can provide valuable leadership skills while keeping costs manageable. Alternatively, many professionals are aiming to accelerate their credentials by enrolling in an online masters that can be completed in just one year—ideal for quickly boosting your qualifications.

If your goal is to quickly secure a well-paying tech job, there are degrees you can get online that pay well, including options in information technology, cybersecurity, and more. For those passionate about future-facing fields, exploring an online ai degree program prepares you for roles in artificial intelligence and machine learning—some of the fastest-growing areas in tech.

Whether you’re looking to specialize further or diversify your skills, these affordable and flexible online options can help you achieve your career goals in the evolving landscape of computer science.