Data-Driven Lipschitz Continuity: A Cost-Effective Approach to Improve
Adversarial Robustness

Data-Driven Lipschitz Continuity: A Cost-Effective Approach to Improve Adversarial Robustness

28 June 2024

Pin-Yu Chen

Papers citing "Data-Driven Lipschitz Continuity: A Cost-Effective Approach to Improve Adversarial Robustness"

6 / 6 papers shown

Title
Enhancing adversarial robustness in Natural Language Inference using explanations Alexandros Koulakos Maria Lymperaiou Giorgos Filandrianos Giorgos Stamou SILM AAML 27 0 0 11 Sep 2024
Efficient and Effective Augmentation Strategy for Adversarial Training Sravanti Addepalli Samyak Jain R. Venkatesh Babu AAML 60 57 0 27 Oct 2022
Admix: Enhancing the Transferability of Adversarial Attacks Xiaosen Wang Xu He Jingdong Wang Kun He AAML 68 192 0 31 Jan 2021
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 213 668 0 19 Oct 2020
Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning Inaam Ilahi Muhammad Usama Junaid Qadir M. Janjua Ala I. Al-Fuqaha D. Hoang Dusit Niyato AAML 55 129 0 27 Jan 2020
You Only Look Once: Unified, Real-Time Object Detection Joseph Redmon S. Divvala Ross B. Girshick Ali Farhadi ObjD 281 35,677 0 08 Jun 2015