Agent4FaceForgery：面向真实场景人脸伪造检测的多智能体大语言模型框架
Agent4FaceForgery: Multi-Agent LLM Framework for Realistic Face Forgery Detection
用 LLM 多智能体在模拟社交环境中合成带细粒度文本-图像一致性标注的伪造数据，并用自适应拒绝采样保证质量与多样性，可直接作为深度伪造检测器的训练数据增强管线复用。
原文依据 An Adaptive Rejection Sampling (ARS) mechanism ensures data quality and diversity.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论人脸伪造检测面临一个关键挑战：离线基准与真实场景效果之间存在持续差距，我们将此归因于训练数据的生态效度不足。本工作提出 Agent4FaceForgery 以解决两个根本问题：（1）如何捕捉人类伪造创作过程中多样的意图与迭代过程；（2）如何建模社交媒体中伴随伪造内容的复杂且往往具有对抗性的文本-图像交互。为此，我们提出一个多智能体框架，由配备画像与记忆模块的大语言模型驱动的智能体模拟伪造创作过程。关键在于，这些智能体在模拟社交环境中交互，以生成带有细粒度文本-图像一致性标注的样本，从而超越简单的二分类。自适应拒绝采样（ARS）机制确保数据质量与多样性。大量实验验证，由我们的仿真驱动方法生成的数据能为多种架构的检测器带来显著性能提升，充分证明了本框架的有效性与价值。
英文原文摘要 1117 字符
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-powered agents, 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 simulation-driven approach brings significant performance gains to detectors of multiple architectures, fully demonstrating the effectiveness and value of our framework.