UniShield: An Adaptive Multi-Agent Framework for Unified Forgery Image Detection and Localization
This addresses the societal risks of misinformation and fraud from synthetic images, offering a unified solution for practical applications, though it appears incremental as it builds on existing detection methods.
The paper tackles the problem of detecting and localizing forged images across diverse domains by proposing UniShield, a multi-agent framework that integrates perception and detection agents, achieving state-of-the-art results in experiments.
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. The perception agent intelligently analyzes image features to dynamically select suitable detection models, while the detection agent consolidates various expert detectors into a unified framework and generates interpretable reports. Extensive experiments show that UniShield achieves state-of-the-art results, surpassing both existing unified approaches and domain-specific detectors, highlighting its superior practicality, adaptiveness, and scalability.