Indicators of Attack Failure: Debugging and Improving Optimization of
Adversarial Examples

Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples

18 June 2021

Maura Pintor

Ambra Demontis

Nicholas Carlini

Battista Biggio

Fabio Roli

Papers citing "Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples"

8 / 8 papers shown

Title
AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples Antonio Emanuele Cinà Jérôme Rony Maura Pintor Luca Demetrio Ambra Demontis Battista Biggio Ismail Ben Ayed Fabio Roli ELM AAML SILM 44 8 0 30 Apr 2024
The Adaptive Arms Race: Redefining Robustness in AI Security Ilias Tsingenopoulos Vera Rimmer Davy Preuveneers Fabio Pierazzi Lorenzo Cavallaro Wouter Joosen AAML 72 0 0 20 Dec 2023
"Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice Giovanni Apruzzese Hyrum S. Anderson Savino Dambra D. Freeman Fabio Pierazzi Kevin A. Roundy AAML 31 75 0 29 Dec 2022
Increasing Confidence in Adversarial Robustness Evaluations Roland S. Zimmermann Wieland Brendel Florian Tramèr Nicholas Carlini AAML 36 16 0 28 Jun 2022
Evaluating the Adversarial Robustness of Adaptive Test-time Defenses Francesco Croce Sven Gowal T. Brunner Evan Shelhamer Matthias Hein A. Cemgil TTA AAML 181 67 0 28 Feb 2022
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 234 678 0 19 Oct 2020
A New Defense Against Adversarial Images: Turning a Weakness into a Strength Tao Yu Shengyuan Hu Chuan Guo Wei-Lun Chao Kilian Q. Weinberger AAML 58 101 0 16 Oct 2019
Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression Nilaksh Das Madhuri Shanbhogue Shang-Tse Chen Fred Hohman Siwei Li Li-Wei Chen Michael E. Kounavis Duen Horng Chau FedML AAML 43 224 0 19 Feb 2018