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Review paper details One-Pixel Attacks and proposes AI security regulation

By PulseAugur Editorial · [1 sources] ·

A new review paper published on arXiv details the research landscape of One-Pixel Attacks (OPAs), a type of adversarial attack in deep learning where modifying a single pixel can cause misclassification. The paper synthesizes studies from 2017 to 2026, offering a taxonomy of OPA research, including algorithmic foundations, defense mechanisms, and domain-specific vulnerabilities. It highlights the prevalence of Differential Evolution-based strategies and identifies persistent gaps in dataset diversity and evaluation standards. The review also proposes a regulatory framework for AI security governance. AI

IMPACT Highlights vulnerabilities in AI systems and proposes regulatory frameworks for AI security.

RANK_REASON The item is a comprehensive review paper published on arXiv. [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 →

Review paper details One-Pixel Attacks and proposes AI security regulation

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The item is a comprehensive review paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Mirza Niaz Morshed, Md. Masudul Islam, Galib Muhammad Shahriar Himel, Md. Aslam Uddin, Hui Liu, Md. Shafiqul Islam ·

    A Comprehensive Review of One-Pixel Attack: Research Status, Taxonomy, Applications, Regulation Policy and Future Directions

    arXiv:2610.00125v1 Announce Type: cross Abstract: One-Pixel Attacks (OPAs) represent one of the most extreme demonstrations of adversarial fragility in deep learning, where modifying a single pixel can reliably induce high-confidence misclassification across domains such as medic…

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