基于结构信息原则的积极机器人检测
IEEE transactions on pattern analysis and machine intelligence
|December 22, 2025
概括
我们介绍SIAMD,这是一个用于社交机器人检测的新框架. 它使用结构信息和对抗性学习来模拟机器人的行为,以主动识别和对抗社交媒体上的复杂机器人.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 社交网络分析 社交网络分析
背景情况:
- 机器人检测对于社交媒体真实性至关重要.
- 复杂的机器人逃避目前的检测方法,创造军备竞赛.
- 现有的方法与不断发展的机器人行为作斗争.
研究的目的:
- 提出一个新的框架,SIAMD,用于积极的社交机器人检测.
- 用结构信息和对抗性学习有效地模拟机器人的行为.
- 为了提高机器人检测的通用性,稳定性和可解释性.
主要方法:
- 将用户帐户和消息交互组织成一个异质结构.
- 使用结构量化历史活动不确定性.
- 利用大型语言模型进行合成内容生成和网络演变.
主要成果:
- SIAMD显著超过了最先进的机器人检测基线.
- 在真实世界数据集上证明了有效性,可概括性和稳定性.
- 通过对抗网络演变实现了增强的主动检测.
结论:
- SIAMD提供了一种强大而有效的社交机器人检测方法.
- 该框架的对抗性质提高了它对抗复杂机器人的能力.
- 结构信息原则和LLM集成提升机器人检测能力.
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