A Geometrical Approach to Evaluate the Adversarial Robustness of Deep
Neural Networks

A Geometrical Approach to Evaluate the Adversarial Robustness of Deep Neural Networks

10 October 2023

Yang Wang

Xin Yang

Papers citing "A Geometrical Approach to Evaluate the Adversarial Robustness of Deep Neural Networks"

5 / 5 papers shown

Title
Rethinking Robustness in Machine Learning: A Posterior Agreement Approach João B. S. Carvalho Alessandro Torcinovich Victor Jimenez Rodriguez Antonio Emanuele Cinà Carlos Cotrini Lea Schönherr J. M. Buhmann OOD 68 0 0 20 Mar 2025
Frame-Event Alignment and Fusion Network for High Frame Rate Tracking Jiqing Zhang Yuanchen Wang Wenxi Liu Meng Li Jinpeng Bai Baocai Yin Xin Yang 37 33 0 25 May 2023
Object Tracking by Jointly Exploiting Frame and Event Domain Jiqing Zhang Xin Yang Yingkai Fu Xiaopeng Wei Baocai Yin B. Dong 74 84 0 19 Sep 2021
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 228 1,835 0 03 Feb 2017
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 284 5,835 0 08 Jul 2016