Mind the box: $l_1$-APGD for sparse adversarial attacks on image
classifiers

Mind the box: $l_1$ -APGD for sparse adversarial attacks on image classifiers

1 March 2021

Francesco Croce

Matthias Hein

Papers citing "Mind the box: $l_1$-APGD for sparse adversarial attacks on image classifiers"

9 / 9 papers shown

Title
FAIR-TAT: Improving Model Fairness Using Targeted Adversarial Training Tejaswini Medi Steffen Jung M. Keuper AAML 21 3 0 30 Oct 2024
The Uncanny Valley: Exploring Adversarial Robustness from a Flatness Perspective Nils Philipp Walter Linara Adilova Jilles Vreeken Michael Kamp AAML 33 1 0 27 May 2024
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 6 0 30 Apr 2024
BruSLeAttack: A Query-Efficient Score-Based Black-Box Sparse Adversarial Attack Viet Vo Ehsan Abbasnejad D. Ranasinghe AAML 16 5 0 08 Apr 2024
Can we achieve robustness from data alone? Nikolaos Tsilivis Jingtong Su Julia Kempe OOD DD 31 18 0 24 Jul 2022
Sparse Visual Counterfactual Explanations in Image Space Valentyn Boreiko Maximilian Augustin Francesco Croce Philipp Berens Matthias Hein BDL CML 23 25 0 16 May 2022
On the Impact of Hard Adversarial Instances on Overfitting in Adversarial Training Chen Liu Zhichao Huang Mathieu Salzmann Tong Zhang Sabine Süsstrunk AAML 8 13 0 14 Dec 2021
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 207 668 0 19 Oct 2020
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 250 5,813 0 08 Jul 2016