  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.
