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Survey details transferable AI attacks and proposes unified taxonomy

By PulseAugur Editorial · [1 sources] ·

A new survey paper published on arXiv details transferable attacks on AI systems, which allow adversarial manipulations from one setting to compromise other AI systems. The paper proposes a unified taxonomy to categorize these attacks across various dimensions such as cross-instance, cross-domain, and cross-model. It also connects transfer mechanisms like gradient alignment and shared representations to enhancement methods, while identifying inconsistencies in current research and proposing a future research agenda. AI

IMPACT Provides a unified framework for understanding and assessing AI vulnerabilities across different systems and attack types.

RANK_REASON The cluster contains a survey paper on AI security research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

Survey details transferable AI attacks and proposes unified taxonomy

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The cluster contains a survey paper on AI security research. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Guangjing Wang, Ce Zhou, Yuanda Wang, Bocheng Chen, Hanqing Guo, Qiben Yan ·

    Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems

    arXiv:2311.11796v3 Announce Type: replace-cross Abstract: Transferable attacks allow adversarial manipulations or learned attack mechanisms constructed in one setting to compromise other AI systems, potentially reducing attack costs and amplifying security and privacy risks. Yet …

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