This SoK paper introduces the first systematic framework covering security pillars, attack landscape, and defense landscape for mobile on-device AI systems while identifying research gaps.
Tinyml security: Explor- ing vulnerabilities in resource-constrained machine learning systems
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
A systematic review of on-device AI inference security finds defenses are imbalanced, with roughly half focused on IP theft while one-third of attacks (adversarial examples) lack any associated defenses.
citing papers explorer
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SoK: Attack and Defense Landscape of Mobile On-device AI Systems
This SoK paper introduces the first systematic framework covering security pillars, attack landscape, and defense landscape for mobile on-device AI systems while identifying research gaps.
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Protecting On-Device AI Inference: A Systematic Review of Attacks and Defence Mechanisms
A systematic review of on-device AI inference security finds defenses are imbalanced, with roughly half focused on IP theft while one-third of attacks (adversarial examples) lack any associated defenses.