用对抗方法生成并检测假社交账号内容，提升真实场景识别能力。
Adversarial Creation and Detection of AI-Generated Social Bot Content
- 构建对抗性数据集，模拟恶意者伪装真人发帖
- 在跨平台多语言数据上实现高精度检测
- 适合安全研究与平台反作弊系统开发者
大型语言模型与社交机器人结合，使恶意行为者能大规模生成类人内容，扰乱信息生态。现有内容检测模型在真实场景中表现不佳，主要因缺乏真实标注数据。本文提出一种对抗性方法，模拟恶意者对真实社交媒体用户的模仿行为，构建了一个多语言、跨平台的成对人类与AI生成消息数据集。基于该对抗数据训练的模型，在真实世界、分布外的数据上显著优于现有内容型机器人检测模型。
原文摘要 · Abstract (English)
The convergence of large language models and social bots allows malicious actors to manipulate the information ecosystem by generating human-like content at scale. Existing models for detecting AI-generated content often fail in the wild, primarily due to the lack of ground-truth data. We address this gap through an adversarial methodology that models the impersonation of real social media users by malicious actors. Using this methodology, we curate a multilingual, cross-platform dataset of paired human and AI-generated messages. Training on such adversarial data yields accurate detection of AI-generated text. Our approach significantly outperforms existing models for content-based bot detection in real-world, out-of-distribution data.
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