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

Published: 20 April 2018 Publication History

Abstract

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.

References

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Lizzie Dearden. 2015. The fake refugee images that are being used to distort public opinion on asylum seekers. The Independent (September 2015).
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Robert Hariman and John Louis Lucaites. 2007. No caption needed: Iconic photographs, public culture, and liberal democracy. University of Chicago Press.
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Linda A. Henkel and Mark E. Mattson. 2011. Reading is believing: The truth effect and source credibility. Consciousness and Cognition 20, 4 (2011), 1705--1721.
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Robert A. Nash, Kimberley A. Wade, and Rebecca J. Brewer. 2009. Why do doctored images distort memory? Consciousness and Cognition 18, 3 (2009), 773--780.
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Sophie J. Nightingale, Kimberley A. Wade, and Derrick G. Watson. 2017. Can people identify original and manipulated photos of real-world scenes? Cognitive Research: Principles and Implications 2, 1 (2017), 30.
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J. W. Peters. 2010. On The Economist's cover, only a part of the picture. (July 5 2010).
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  • (2024)When Readers Do Not Fight Falsehood: An Exploration of Factors Influencing the Perceived Realism of False News on International DisputesSocial Sciences10.3390/socsci1312062913:12(629)Online publication date: 22-Nov-2024
  • (2024)Visual media literacy: educational strategies to combat image and video disinformation on social mediaFrontiers in Communication10.3389/fcomm.2024.14907989Online publication date: 27-Nov-2024
  • (2024)After Deception: How Falling for a Deepfake Affects the Way We See, Hear, and Experience MediaThe International Journal of Press/Politics10.1177/1940161224123353930:1(187-210)Online publication date: 13-Mar-2024
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  1. Seeing Is Believing: How People Fail to Identify Fake Images on the Web

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    cover image ACM Conferences
    CHI EA '18: Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems
    April 2018
    3155 pages
    ISBN:9781450356213
    DOI:10.1145/3170427
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Publication History

    Published: 20 April 2018

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    Author Tags

    1. credibility evaluation
    2. fake images
    3. focus study
    4. image credibility
    5. image manipulation
    6. online images

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    CHI EA '18 Paper Acceptance Rate 1,208 of 3,955 submissions, 31%;
    Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

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    View all
    • (2024)When Readers Do Not Fight Falsehood: An Exploration of Factors Influencing the Perceived Realism of False News on International DisputesSocial Sciences10.3390/socsci1312062913:12(629)Online publication date: 22-Nov-2024
    • (2024)Visual media literacy: educational strategies to combat image and video disinformation on social mediaFrontiers in Communication10.3389/fcomm.2024.14907989Online publication date: 27-Nov-2024
    • (2024)After Deception: How Falling for a Deepfake Affects the Way We See, Hear, and Experience MediaThe International Journal of Press/Politics10.1177/1940161224123353930:1(187-210)Online publication date: 13-Mar-2024
    • (2024)Photorealism versus photography. AI-generated depiction in the age of visual disinformationJournal of Aesthetics & Culture10.1080/20004214.2024.234078716:1Online publication date: 10-Apr-2024
    • (2024)Human detection of political speech deepfakes across transcripts, audio, and videoNature Communications10.1038/s41467-024-51998-z15:1Online publication date: 2-Sep-2024
    • (2024)Misinformation or hard to tell? An eye-tracking study to investigate the effects of food crisis misinformation on social media engagementPublic Relations Review10.1016/j.pubrev.2024.10248350:4(102483)Online publication date: Nov-2024
    • (2024)Big Data Approaches to Identifying Crisis MisinformationCommunication and Misinformation10.1002/9781394184972.ch13(196-214)Online publication date: 6-Dec-2024
    • (2023)Assessing the perceived credibility of deepfakes: The impact of system-generated cues and video characteristicsNew Media & Society10.1177/14614448231199664Online publication date: 25-Sep-2023
    • (2023)Referencing in YouTube Knowledge Communication VideosProceedings of the 2023 ACM International Conference on Interactive Media Experiences10.1145/3573381.3596163(60-70)Online publication date: 12-Jun-2023
    • (2023)Multimodal analysis of disinformation and misinformationRoyal Society Open Science10.1098/rsos.23096410:12Online publication date: 20-Dec-2023
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