Objective Metrics and Gradient Descent Algorithms for Adversarial Examples in Machine Learning. - Uyeong Jang; Xi Wu 001; Somesh Jha (ACSAC, 2017)

Manifold Assumption and Defenses Against Adversarial Perturbations. - Xi Wu 001; Uyeong Jang; Lingjiao Chen; Somesh Jha (CoRR, 2017)

Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training. - Xi Wu 001; Uyeong Jang; Jiefeng Chen 001; Lingjiao Chen; Somesh Jha (ICML, 2018)

Detecting Adversarial Examples Using Data Manifolds. - Susmit Jha; Uyeong Jang; Somesh Jha; Brian Jalaian (MILCOM, 2018)

On Need for Topology Awareness of Generative Models. - Uyeong Jang; Susmit Jha; Somesh Jha (CoRR, 2019)

Generating Semantic Adversarial Examples with Differentiable Rendering. - Lakshya Jain; Wilson Wu; Steven Chen; Uyeong Jang; Varun Chandrasekaran; Sanjit A. Seshia; Somesh Jha (CoRR, 2019)

On the Need for Topology-Aware Generative Models for Manifold-Based Defenses. - Uyeong Jang; Susmit Jha; Somesh Jha (ICLR, 2020)

Holistic Cube Analysis: A Query Framework for Data Insights. - Xi Wu 001; Joe Benassi; Yaqi Zhang; Uyeong Jang; James Foster; Stella Kim; Yujing Sun; Somesh Jha; John Cieslewicz; Jeffrey F. Naughton (CoRR, 2023)

Bilevel Relations and Their Applications to Data Insights. - Xi Wu 001; Xiangyao Yu; Shaleen Deep; Ahmed Mahmood; Uyeong Jang; Stratis Viglas; Somesh Jha; John Cieslewicz; Jeffrey F. Naughton (CoRR, 2023)