Seeing Is Believing: How People Fail to Identify Fake Images on the Web
Open accessAbstract
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.
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People rarely question the authenticity of images even when primed.
“People rarely question the authenticity of images even when primed39.”
From Human detection of political speech deepfakes across transcripts, audio, and video (2024) · cites this paper as [39]
Past work in the cognitive science of media forensics has demonstrated that people are not good at perceiving and reasoning about shadow, reflection, and other physical implausibility cues.
“Past work in the cognitive science of media forensics has demonstrated that people are not good at perceiving and reasoning about shadow, reflection, and other physical implausibility cues (9–12).”
From Deepfake detection by human crowds, machines, and machine-informed crowds (2022) · cites this paper as [12]
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- Authors
- Kasra, M., Shen, C., & O'Brien, J. F.
- Affiliations
- University of Virginia (US)
- University of California, Davis (US)
- University of California, Berkeley, Berkeley, CA, USA
- Venue
- Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2018-04-20
- Pages
- 1-6
- OpenAlex
- W2799130598
- Cited by
- 36
- References
- 8
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 7 · 2023: 4 · 2022: 7 · 2021: 6 · 2020: 1 · 2019: 3 · 2018: 1
- FWCI
- 8.38
- Type
- conference paper
- Language
- en
- Open access
- green
- escholarship.org
- Locations
- CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Science Foundation (CNS- 1444840)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Keywords
- image editing, image tampering, visual misinformation, image authenticity, credibility assessment, deception detection, post hoc analysis, priming
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