构建数字虚伪:比较用户检测人工智能生成的面孔与基于消息的诈骗方法
Dawn M Sarno1, John Solorio1, Shea Ballar1
1Clemson University, United States of America.
Acta psychologica
|December 5, 2025
概括
与网络鱼骗局相比,用户很难检测人工智能生成的面孔. 风险识别和认知反射等个体差异影响了数字虚假的检测,突出了需要采取保障措施的需要.
科学领域:
- 认知心理学 认知心理学
- 人与计算机的交互
- 网络安全 网络安全
背景情况:
- 生成型人工智能 (genAI) 通过图像和消息实现了真实的假象.
- 检测基于消息的不真实性 (例如,网络鱼) 被研究,但基于图像的检测被理解得更少.
- 跨模式检测数字虚伪的基础认知机制尚不清楚.
研究的目的:
- 检查用户在检测图像与基于消息的不真实性方面的相似性.
- 调查是否个体差异预测易受两种类型的不真实性.
- 确定与数字虚假检测相关的其他个体差异.
主要方法:
- 参与者分类真实与网络鱼消息 (电子邮件/文本).
- 参与者分类了真实与人工智能生成的面孔.
- 评估认知推理,数字/风险素养和人口统计.
主要成果:
- 用户在检测基于图像的不真实性 (AI面孔) 方面不如基于消息的诈骗 (网络鱼) 熟练.
- 对人工智能生成的面部进行了较差,更有风险的分类.
- 风险识字,认知反射和性别与检测基于图像的不真实性相关.
结论:
- 数字虚假的检测因模式而异,图像带来了更大的挑战.
- 个人差异在识别人工智能产生的不真实性方面发挥着作用.
- 基因AI的持续进步需要改进数字安全的技术保障和用户干预.
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