Papers for

ai robustness engineers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Stroke attack uses simple occlusion patches to fool AI image classifiers

STROKE: Simple Sparse Structural Occlusions for Black-Box Adversarial Attacks

Abstract: We introduce STROKE, a query-efficient score-based black-box adversarial attack that constructs perturbations from simple oriented rectangular occlusions. STROKE optimizes these perturbations through a lightweight greedy add-and-refine procedure. Across diverse image classifiers, STROKE achieves high attack success with low query counts at nominal 1--2\% area settings, showing favorable performance relative to existing patch-based attacks in several configurations. Matched comparisons further demonstrate substantial reductions in wall-clock attack time, while ablation studies examine the key design choices and effectiveness across structural primitives. These findings suggest that simple structural occlusions offer an effective and computationally efficient search space for black-box adversarial attacks.

Fri 23 OctComputer Vision and Pattern RecognitionCryptography and SecurityHuman-Computer Interaction

The gist

It can be hard for some AI systems to recognize images correctly when small parts are blocked or altered. The authors introduce STROKE, a method that attacks image recognition by covering parts of an image with simple rectangular shapes. This approach finds effective ways to confuse AI quickly and with few guesses. Because it uses simple shapes and a fast search, it can fool many different classifiers efficiently.

Open → 2610.32932v1