AgentFoX: LLM Agent-Guided Fusion with eXplainability for AI-Generated Image Detection

• URL: http://arxiv.org/abs/2603.23115v1
• Date: Tue, 24 Mar 2026 12:10:21 GMT
• Title: AgentFoX: LLM Agent-Guided Fusion with eXplainability for AI-Generated Image Detection
• Abstract summary: We present textbfAgentFoX, a framework that redefines AIGI detection as a dynamic, multi-phase analytical process.<n>Our approach employs a quick-integration fusion mechanism guided by a curated knowledge base.<n>AgentFoX produces a detailed, human-readable forensic report that substantiates its verdict.
• Score: 21.02452809723292
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The increasing realism of AI-Generated Images (AIGI) has created an urgent need for forensic tools capable of reliably distinguishing synthetic content from authentic imagery. Existing detectors are typically tailored to specific forgery artifacts--such as frequency-domain patterns or semantic inconsistencies--leading to specialized performance and, at times, conflicting judgments. To address these limitations, we present \textbf{AgentFoX}, a Large Language Model-driven framework that redefines AIGI detection as a dynamic, multi-phase analytical process. Our approach employs a quick-integration fusion mechanism guided by a curated knowledge base comprising calibrated Expert Profiles and contextual Clustering Profiles. During inference, the agent begins with high-level semantic assessment, then transitions to fine-grained, context-aware synthesis of signal-level expert evidence, resolving contradictions through structured reasoning. Instead of returning a coarse binary output, AgentFoX produces a detailed, human-readable forensic report that substantiates its verdict, enhancing interpretability and trustworthiness for real-world deployment. Beyond providing a novel detection solution, this work introduces a scalable agentic paradigm that facilitates intelligent integration of future and evolving forensic tools.

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