Towards the Development of Robust Deep Neural Networks in Adversarial Settings. - Todd P. Huster; Cho-Yu Jason Chiang; Ritu Chadha; Ananthram Swami (MILCOM, 2018)

Limitations of the Lipschitz Constant as a Defense Against Adversarial Examples. - Todd Huster; Cho-Yu Jason Chiang; Ritu Chadha (Nemesis/UrbReas/SoGood/IWAISe/GDM@PKDD/ECML, 2018)

Universal Lipschitz Approximation in Bounded Depth Neural Networks. - Jeremy E. J. Cohen; Todd Huster; Ra Cohen (CoRR, 2019)

Robot Design With Neural Networks, MILP Solvers and Active Learning. - Sanjai Narain; Emily Mak; Dana Chee; Todd Huster; Jeremy E. J. Cohen; Kishore Pochiraju; Brendan J. Englot; Niraj K. Jha; Karthik S. Narayan (CoRR, 2020)

Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions. - Todd Huster; Jeremy E. J. Cohen; Zinan Lin 001; Kevin Chan 001; Charles A. Kamhoua; Nandi O. Leslie; Cho-Yu Jason Chiang; Vyas Sekar (ICML, 2021)

TOP: Backdoor Detection in Neural Networks via Transferability of Perturbation. - Todd Huster; Emmanuel Ekwedike (CoRR, 2021)

Scaled-Time-Attention Robust Edge Network. - Richard Lau; Lihan Yao; Todd Huster; William Johnson; Stephen Arleth; Justin Wong; Devin B. Ridge; Michael Fletcher; William C. Headley (CoRR, 2021)

ReFace: Real-time Adversarial Attacks on Face Recognition Systems. - Shehzeen Hussain; Todd Huster; Chris Mesterharm; Paarth Neekhara; Kevin An; Malhar Jere; Harshvardhan Sikka; Farinaz Koushanfar (CoRR, 2022)

ReFace: Adversarial Transformation Networks for Real-time Attacks on Face Recognition Systems. - Shehzeen Hussain; Todd Huster; Chris Mesterharm; Paarth Neekhara; Farinaz Koushanfar (DSN, 2023)

Solving Trojan Detection Competitions with Linear Weight Classification. - Todd Huster; Peter Lin; Razvan Stefanescu; Emmanuel Ekwedike; Ritu Chadha (CoRR, 2024)