McDaniel, Patrick

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

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

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

A New Era in LLM Security: Exploring Security Concerns in Real-World LLM-based Systems

Wu, F., Zhang, N., Jha, S., McDaniel, P., & Xiao, C. (2024) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 23