Papernot, Nicolas

The Limitations of Deep Learning in Adversarial Settings

Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z. B., & Swami, A. (2016) · 2016 IEEE European Symposium on Security and Privacy (EuroS&P)

Adversarial Robustness in Machine LearningDigital Media Forensic Detection

Cited by 3,993

Practical Black-Box Attacks against Machine Learning

Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z. B., & Swami, A. (2017) · Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 3,495

Ensemble Adversarial Training: Attacks and Defenses

Tramèr, F., Kurakin, A., Papernot, N., Goodfellow, I., Boneh, D., & McDaniel, P. (2017) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 2,176

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

Papernot, N., McDaniel, P., & Goodfellow, I. (2016) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 1,326

Machine Unlearning

Bourtoule, L., Chandrasekaran, V., Choquette-Choo, C. A., Jia, H., Travers, A., Zhang, B., Lie, D., & Papernot, N. (2021) · 2021 IEEE Symposium on Security and Privacy (SP)

Privacy-Preserving Technologies in DataPrivacy, Security, and Data Protection

Cited by 810

AI models collapse when trained on recursively generated data

Shumailov, I., Shumaylov, Z., Zhao, Y., Papernot, N., Anderson, R., & Gal, Y. (2024) · Nature

Topic ModelingEthics and Social Impacts of AI

Cited by 793

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

Papernot, N., Abadi, M., Erlingsson, Ú., Goodfellow, I., & Talwar, K. (2016) · arXiv

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 492

Scalable Private Learning with PATE

Papernot, N., Song, S., Mironov, I., Raghunathan, A., Talwar, K., & Erlingsson, Ú. (2018) · arXiv

Privacy-Preserving Technologies in DataCryptography and Data Security

Cited by 373

Making machine learning robust against adversarial inputs

Goodfellow, I., McDaniel, P., & Papernot, N. (2018) · Communications of the ACM

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 358

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

Shumailov, I., Shumaylov, Z., Zhao, Y., Gal, Y., Papernot, N., & Anderson, R. (2023) · arXiv

Reinforcement Learning in RoboticsScientific Computing and Data Management

Cited by 151

Unrolling SGD: Understanding Factors Influencing Machine Unlearning

Thudi, A., Deza, G., Chandrasekaran, V., & Papernot, N. (2022) · 2022 IEEE 7th European Symposium on Security and Privacy (EuroS&P)

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 135

A General Approach to Adding Differential Privacy to Iterative Training Procedures

McMahan, H. B., Andrew, G., Erlingsson, U., Chien, S., Mironov, I., Papernot, N., & Kairouz, P. (2018) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 126

Sponge Examples: Energy-Latency Attacks on Neural Networks

Shumailov, I., Zhao, Y., Bates, D., Papernot, N., Mullins, R., & Anderson, R. (2021) · 2021 IEEE European Symposium on Security and Privacy (EuroS&P)

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 125

Machine Learning in Adversarial Settings

McDaniel, P., Papernot, N., & Celik, Z. B. (2016) · IEEE Security & Privacy

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 120

Thieves on Sesame Street! Model Extraction of BERT-based APIs

Krishna, K., Tomar, G. S., Parikh, A. P., Papernot, N., & Iyyer, M. (2019) · arXiv

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 111

Bad Characters: Imperceptible NLP Attacks

Boucher, N., Shumailov, I., Anderson, R., & Papernot, N. (2022) · 2022 IEEE Symposium on Security and Privacy (SP)

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 102

On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning

Thudi, A., Jia, H., Shumailov, I., & Papernot, N. (2021) · arXiv

Privacy-Preserving Technologies in DataEthics and Social Impacts of AI

Cited by 48

Tempered Sigmoid Activations for Deep Learning with Differential Privacy

Papernot, N., Thakurta, A., Song, S., Chien, S., & Erlingsson, Ú. (2020) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 23

CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents

Foerster, H., Blanchard, T., Nikolić, K., Shumailov, I., Zhang, C., Mullins, R., Papernot, N., Tramèr, F., & Zhao, Y. (2026) · arXiv

Security and Verification in ComputingAdversarial Robustness in Machine Learning

Cited by 0