Trace-Norm Adversarial Examples
Ehsan Kazemi, Thomas Kerdreux, Liqiang Wang

TL;DR
This paper investigates structured adversarial examples constrained by various norms, highlighting their implications for robustness certification, attack effectiveness, and potential for more perceptible or controllable perturbations.
Contribution
It introduces structured distortion sets for adversarial attacks, challenging existing certification methods and enabling more controllable and potentially larger perturbations.
Findings
Structured attacks can bypass $l_p$ based certification.
Different norms produce diverse adversarial example structures.
Structured perturbations may be more perceptible or controllable.
Abstract
White box adversarial perturbations are sought via iterative optimization algorithms most often minimizing an adversarial loss on a neighborhood of the original image, the so-called distortion set. Constraining the adversarial search with different norms results in disparately structured adversarial examples. Here we explore several distortion sets with structure-enhancing algorithms. These new structures for adversarial examples, yet pervasive in optimization, are for instance a challenge for adversarial theoretical certification which again provides only certificates. Because adversarial robustness is still an empirical field, defense mechanisms should also reasonably be evaluated against differently structured attacks. Besides, these structured adversarial perturbations may allow for larger distortions size than their counter-part while remaining imperceptible or…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Integrated Circuits and Semiconductor Failure Analysis · Medical Imaging Techniques and Applications
