Identifying Adversarially Attackable and Robust Samples

30 January 2023

Papers citing "Identifying Adversarially Attackable and Robust Samples"

7 / 7 papers shown

Title
Fragile Giants: Understanding the Susceptibility of Models to Subpopulation Attacks Isha Gupta Hidde Lycklama Emanuel Opel Evan Rose Anwar Hithnawi AAML 34 0 0 11 Oct 2024
Sample Attackability in Natural Language Adversarial Attacks Vyas Raina Mark J. F. Gales SILM 42 1 0 21 Jun 2023
The Chai Platform's AI Safety Framework Xiaoding Lu Aleksey Korshuk Z. Liu W. Beauchamp 21 2 0 05 Jun 2023
A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologies Zhuang Qian Kaizhu Huang Qiufeng Wang Xu-Yao Zhang OOD AAML ObjD 49 72 0 26 Mar 2022
Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Survey Ngoc Dung Huynh Mohamed Reda Bouadjenek Imran Razzak Kevin Lee Chetan Arora Ali Hassani A. Zaslavsky AAML 29 6 0 22 Feb 2022
Recent Advances in Adversarial Training for Adversarial Robustness Tao Bai Jinqi Luo Jun Zhao B. Wen Qian Wang AAML 82 475 0 02 Feb 2021
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 293 3,112 0 04 Nov 2016