Jha, Somesh

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

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

Semantic Robustness of Models of Source Code

Henkel, J., Ramakrishnan, G., Wang, Z., Albarghouthi, A., Jha, S., & Reps, T. (2022) · 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

Adversarial Robustness in Machine LearningSoftware Engineering Research

Cited by 92

Identifying and Mitigating the Security Risks of Generative AI

Barrett, C., Boyd, B., Bursztein, E., Carlini, N., Chen, B., Choi, J., Chowdhury, A. R., Christodorescu, M., Datta, A., Feizi, S., et al. (2023) · Foundations and Trends® in Privacy and Security

Adversarial Robustness in Machine LearningInformation and Cyber Security

Cited by 57

Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs

Chen, J., Yoon, J., Ebrahimi, S., Arik, S., Pfister, T., & Jha, S. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023

Topic ModelingNatural Language Processing Techniques

Cited by 13

D4: Detection of Adversarial Diffusion Deepfakes Using Disjoint Ensembles

Hooda, A., Mangaokar, N., Feng, R., Fawaz, K., Jha, S., & Prakash, A. (2024) · 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

Digital Media Forensic DetectionAdversarial Robustness in Machine Learning

Cited by 7