培训人类超级识别器检测和歧视人工智能生成的面孔
Katie L H Gray1, Josh P Davis2, Carl Bunce1
1School of Psychology and Clinical Language Sciences, University of Reading, Reading, UK.
Royal Society open science
|November 14, 2025
概括
超级识别者 (SRs) 在检测由生成对抗网络 (GANs) 产生的合成面部方面表现优于典型的个人. 一个简单的培训计划显著提高了SR和对照的检测准确性.
科学领域:
- 计算机视觉 计算机视觉
- 心理学 心理学 心理学
- 人工智能的人工智能
背景情况:
- 生成对抗网络 (GAN) 产生高度现实的合成面孔,这对检测提出了挑战.
- 现有的合成面部检测培训计划显示出不同的结果.
研究的目的:
- 与典型对照者相比,研究超级识别器 (SRs) 的合成面部检测和歧视能力.
- 评估旨在突出染工件的培训程序的有效性.
主要方法:
- 使用了两种实验设计,共有283名SR和381名对照参与者.
- 参与者执行了合成面部检测和歧视任务,有或没有具体的培训干预.
主要成果:
- 与对照参与者相比,SR在检测和区分合成面部方面表现出更好的表现.
- 对照参与者在没有训练的情况下表现低于偶然,但在训练后显著改善.
- 培训提高了SR和对照组的表现,在不同组中改善的幅度相似.
结论:
- 似乎SRs利用线索超越染文物合成面部检测.
- 一个简单的训练程序可以显著提高合成面部检测在SR和典型个体的准确性.
- 经过培训的SR提供了合成媒体法医学的现实应用的潜力.
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