用多智能体模拟真实伪造过程，提升人脸伪造检测效果。
Agent4FaceForgery: Multi-Agent LLM Framework for Realistic Face Forgery Detection
- 构建多智能体LLM框架，模拟人类伪造的多样意图与迭代过程。
- 生成带文本-图像一致性标签的数据，推动检测器性能提升23.6%以上。
- 适合需要高鲁棒性伪造检测系统的研发团队使用。
人脸伪造检测面临离线基准与实际应用间巨大效能差距，根源在于训练数据生态无效性。本文提出Agent4FaceForgery框架，解决两大核心问题：(1) 如何捕捉人类伪造创作中的多样意图与迭代过程；(2) 如何建模社交媒体中伪造内容常见的复杂、对抗性文本-图像交互。为此，我们设计多智能体系统，由具备身份与记忆模块的LLM驱动，在模拟社交环境中协作生成带细粒度文本-图像一致性标签的数据，突破传统二分类局限。引入自适应拒绝采样（ARS）机制保障数据质量与多样性。大量实验证明，该仿真驱动生成的数据使多种架构检测器性能显著提升，充分验证了框架的有效性与价值。
原文摘要 · Abstract (English)
Face forgery detection faces a critical challenge: a persistent gap between offline benchmarks and real-world efficacy,which we attribute to the ecological invalidity of training data.This work introduces Agent4FaceForgery to address two fundamental problems: (1) how to capture the diverse intents and iterative processes of human forgery creation, and (2) how to model the complex, often adversarial, text-image interactions that accompany forgeries in social media. To solve this,we propose a multi-agent framework where LLM-poweredagents, equipped with profile and memory modules, simulate the forgery creation process. Crucially, these agents interact in a simulated social environment to generate samples labeled for nuanced text-image consistency, moving beyond simple binary classification. An Adaptive Rejection Sampling (ARS) mechanism ensures data quality and diversity. Extensive experiments validate that the data generated by our simulationdriven approach brings significant performance gains to detectors of multiple architectures, fully demonstrating the effectiveness and value of our framework.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。