Fredrikson, Matt

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

Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures

Fredrikson, M., Jha, S., & Ristenpart, T. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 2,961

Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting

Yeom, S., Giacomelli, I., Fredrikson, M., & Jha, S. (2018) · 2018 IEEE 31st Computer Security Foundations Symposium (CSF)

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 1,001

Universal and Transferable Adversarial Attacks on Aligned Language Models

Zou, A., Wang, Z., Carlini, N., Nasr, M., Kolter, J. Z., & Fredrikson, M. (2023) · arXiv

Adversarial Robustness in Machine LearningSpeech Recognition and Synthesis

Cited by 197

Stolen Memories: Leveraging Model Memorization for Calibrated White-Box Membership Inference

Leino, K., & Fredrikson, M. (2019) · arXiv

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 145

Representation Engineering: A Top-Down Approach to AI Transparency

Zou, A., Phan, L., Chen, S., Campbell, J., Guo, P., Ren, R., Pan, A., Yin, X., Mazeika, M., Dombrowski, A.-K., et al. (2023) · arXiv

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 65

Improving Alignment and Robustness with Circuit Breakers

Zou, A., Phan, L., Wang, J., Duenas, D., Lin, M., Andriushchenko, M., Wang, R., Kolter, Z., Fredrikson, M., & Hendrycks, D. (2024) · arXiv

Software System Performance and ReliabilityDistributed systems and fault tolerance

Cited by 19