The Adaptive Arms Race: Redefining Robustness in AI Security

20 December 2023

Papers citing "The Adaptive Arms Race: Redefining Robustness in AI Security"

8 / 8 papers shown

Title
Stateful Defenses for Machine Learning Models Are Not Yet Secure Against Black-box Attacks Ryan Feng Ashish Hooda Neal Mangaokar Kassem Fawaz S. Jha Atul Prakash AAML 24 10 0 11 Mar 2023
Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems Chawin Sitawarin Florian Tramèr Nicholas Carlini AAML 38 6 0 07 Oct 2022
On the Convergence and Robustness of Adversarial Training Yisen Wang Xingjun Ma James Bailey Jinfeng Yi Bowen Zhou Quanquan Gu AAML 153 314 0 15 Dec 2021
A Unified Game-Theoretic Interpretation of Adversarial Robustness Jie Ren Die Zhang Yisen Wang Lu Chen Zhanpeng Zhou ... Xu Cheng Xin Eric Wang Meng Zhou Jie Shi Quanshi Zhang AAML 24 21 0 12 Mar 2021
On the Effectiveness of Small Input Noise for Defending Against Query-based Black-Box Attacks Junyoung Byun Hyojun Go Changick Kim AAML 82 17 0 13 Jan 2021
AutoDropout: Learning Dropout Patterns to Regularize Deep Networks Hieu H. Pham Quoc V. Le 26 50 0 05 Jan 2021
Sign-OPT: A Query-Efficient Hard-label Adversarial Attack Minhao Cheng Simranjit Singh Patrick H. Chen Pin-Yu Chen Sijia Liu Cho-Jui Hsieh AAML 90 197 0 24 Sep 2019
Neural Architecture Search with Reinforcement Learning Barret Zoph Quoc V. Le 246 5,046 0 05 Nov 2016