Computer Graphics

University of California - Berkeley

Seeing Is Believing: How People Fail to Identify Fake Images on the Web


The growing ease with which digital images can be convincingly manipulated and widely distributed on the Internet makes viewers increasingly susceptible to visual misinformation and deception. In situations where ill-intentioned individuals seek to deliberately mislead and influence viewers through fake online images, the harmful consequences could be substantial. We describe an exploratory study of how individuals react, respond to, and evaluate the authenticity of images that accompany online stories in Internet-enabled communications channels. Our preliminary findings support the assertion that people perform poorly at detecting skillful image manipulation, and that they often fail to question the authenticity of images even when primed regarding image forgery through discussion. We found that viewers make credibility evaluation based mainly on non-image cues rather than the content depicted. Moreover, our study revealed that in cases where context leads to suspicion, viewers apply post-hoc analysis to support their suspicions regarding the authenticity of the image.


Mona Kasra, Cuihua Shen, and James F. O'Brien. "Seeing Is Believing: How People Fail to Identify Fake Images on the Web". In Proceedings of the ACM SIGCHI 2018 Extended Abstracts, pages 1–6, April 2018.