UniShield: Multi-Agent Framework for Universal Forgery Detection
Researchers have proposed UniShield, a novel multi-agent-based unified system for Forgery Image Detection and Localization (FIDL). The framework addresses the limitations of existing domain-specific methods, which suffer from narrow specialization and poor cross-domain generalization. UniShield integrates a perception agent with a detection agent to handle diverse forgery types, including image manipulation, document manipulation, DeepFake, and AI-generated images. The system aims to combat societal risks such as misinformation and fraud posed by increasingly realistic synthetic images.
Key facts
- UniShield is a multi-agent-based unified system for forgery detection and localization.
- It addresses limitations of existing domain-specific detection methods.
- The framework integrates a perception agent with a detection agent.
- It covers diverse forgery domains: image manipulation, document manipulation, DeepFake, and AI-generated images.
- The research was published on arXiv under identifier 2510.03161.
- The work was announced as a replace-cross type on arXiv.
- Synthetic images pose risks like misinformation and fraud.
- FIDL is essential for maintaining information integrity and societal security.
Entities
Institutions
- arXiv