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25 February 2014 Assessing the impact of image manipulation on users' perceptions of deception
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Proceedings Volume 9014, Human Vision and Electronic Imaging XIX; 90140Y (2014)
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States
Generally, we expect images to be an honest reflection of reality. However, this assumption is undermined by the new image editing technology, which allows for easy manipulation and distortion of digital contents. Our understanding of the implications related to the use of a manipulated data is lagging behind. In this paper we propose to exploit crowdsourcing tools in order to analyze the impact of different types of manipulation on users’ perceptions of deception. Our goal is to gain significant insights about how different types of manipulations impact users’ perceptions and how the context in which a modified image is used influences human perception of image deceptiveness. Through an extensive crowdsourcing user study, we aim at demonstrating that the problem of predicting user-perceived deception can be approached by automatic methods. Analysis of results collected on Amazon Mechanical Turk platform highlights how deception is related to the level of modifications applied to the image and to the context within modified pictures are used. To the best of our knowledge, this work represents the first attempt to address to the image editing debate using automatic approaches and going beyond investigation of forgeries.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Valentina Conotter, Duc-Tien Dang-Nguyen, Giulia Boato, María Menéndez, and Martha Larson "Assessing the impact of image manipulation on users' perceptions of deception", Proc. SPIE 9014, Human Vision and Electronic Imaging XIX, 90140Y (25 February 2014);


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