The Ultimate Combo: Boosting Adversarial Example Transferability by Composing Data Augmentations

no code implementations • 18 Dec 2023 • Zebin Yun, Achi-Or Weingarten, Eyal Ronen, Mahmood Sharif

We also found that the best composition significantly outperformed the state of the art (e. g., 93. 7% vs. $\le$ 82. 7% average transferability on ImageNet from normally trained surrogates to adversarially trained targets).

Adversarial Robustness Data Augmentation

A Simple Explanation for the Existence of Adversarial Examples with Small Hamming Distance

no code implementations • 30 Jan 2019 • Adi Shamir, Itay Safran, Eyal Ronen, Orr Dunkelman

The existence of adversarial examples in which an imperceptible change in the input can fool well trained neural networks was experimentally discovered by Szegedy et al in 2013, who called them "Intriguing properties of neural networks".