论文标题
人群动力的面部操纵检测：融合人类检查员的决定
Crowd-powered Face Manipulation Detection: Fusing Human Examiner Decisions
论文作者
论文摘要
我们研究了融合人类检查者决策的潜力，以实现数字面部操纵检测任务。为此，提出了各种决策融合方法，其中包括审查员的决策信心，经验水平以及他们做出决定的时间。进行的实验是基于对人类数字面部图像操纵检测能力的心理物理评估，其中应用了不同的操纵技术，即面部变形，面部交换和修饰。 223名参与者的决定被融合在一起，以模拟多达七位人类审查员的人群。实验结果表明，（1）尽管单一人类检查员达到了中等的检测性能，但可以通过决策融合获得高精度，以及（2）加权融合，将考官的决策置信度带入了最有竞争力的检测性能。
We investigate the potential of fusing human examiner decisions for the task of digital face manipulation detection. To this end, various decision fusion methods are proposed incorporating the examiners' decision confidence, experience level, and their time to take a decision. Conducted experiments are based on a psychophysical evaluation of digital face image manipulation detection capabilities of humans in which different manipulation techniques were applied, i.e. face morphing, face swapping and retouching. The decisions of 223 participants were fused to simulate crowds of up to seven human examiners. Experimental results reveal that (1) despite the moderate detection performance achieved by single human examiners, a high accuracy can be obtained through decision fusion and (2) a weighted fusion which takes the examiners' decision confidence into account yields the most competitive detection performance.
