Identifying Computer-Generated Portraits: The Importance of Training and Incentives. (September 2017)

• Record Type: Journal Article
• Authors: Mader, Brandon; Banks, Martin S.; Farid, Hany
• Is Part Of: Perception. Volume 46:Number 9(2017)
• Page Start: 1062
• Page End: 1076
• Publication Date: 2017-09
• Subjects: computer graphics -- photo-realistic -- photo forensics; Perception -- Periodicals; Perception -- Periodicals; Perception; Periodicals; 153.705
• DOI: 10.1177/0301006617713633
• Languages: English
• ISSNs: 0301-0066

Abstract

The past two decades have seen remarkable advances in photo-realistic rendering of everything from inanimate objects to landscapes, animals, and humans. We previously showed that despite these tremendous advances, human observers remain fairly good at distinguishing computer-generated from photographic images. Building on these results, we describe a series of follow-up experiments that reveal how to improve observer performance. Of general interest to anyone performing psychophysical studies on Mechanical Turk or similar platforms, we find that observer performance can be significantly improved with the proper incentives.