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New STROKE attack method uses sparse occlusions for efficient black-box adversarial examples

By PulseAugur Editorial · [1 sources] ·

Researchers have developed STROKE, a novel black-box adversarial attack method for image classifiers. This technique utilizes simple, sparse structural occlusions, specifically oriented rectangular shapes, to create perturbations. STROKE demonstrates high attack success rates with significantly fewer queries and reduced computation time compared to existing patch-based attacks, making it an efficient and effective approach for generating adversarial examples. AI

IMPACT Introduces a more efficient method for generating adversarial attacks, potentially impacting model robustness testing and security.

RANK_REASON The cluster contains a research paper detailing a new method for adversarial attacks on computer vision models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New STROKE attack method uses sparse occlusions for efficient black-box adversarial examples

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The cluster contains a research paper detailing a new method for adversarial attacks on computer vision models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Ankit Gupta, Akshat Pandya, Paul Lin, Christoph Adami, Emily Dolson ·

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

    arXiv:2610.32932v1 Announce Type: new 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-ref…

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