UniShield: An Adaptive Multi-Agent Framework for Unified Forgery Image Detection and Localization

Kari Jaaskelainen

06 Oct 2025 — 1 min read

Quick take: UniShield: An Adaptive Multi-Agent Framework for Unified Forgery Image Detection and Localization

With the rapid advancements in image generation, synthetic images have become increasingly realistic, posing significant societal risks, such as misinformation and fraud. Forgery Image Detection and Localization (FIDL) thus emerges as essential for maintaining information integrity and societal security.

Despite impressive performances by existing domain-specific detection methods, their practical applicability remains limited, primarily due to their narrow specialization, poor cross-domain generalization, and the absence of an integrated adaptive framework. To address these issues, we propose UniShield, the novel multi-agent-based unified system capable of detecting and localizing image forgeries across diverse domains, including image manipulation, document manipulation, DeepFake, and AI-generated images. UniShield innovatively integrates a perception agent with a detection agent.

Why it matters: This research may affect how everyday systems stay reliable and safe.

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Paper: http://arxiv.org/abs/2510.03161v1

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