Assessing the impact of image manipulation on users' perceptions of deception

Assessing the impact of image manipulation on users' perceptions of deception

Conotter, V.
Dang-Nguyen, D.T.
Boato, G.
Menendez, M.
Larson, M.

Electrical Engineering, Mathematics and Computer Science

Intelligent Systems

2014-02-25

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.

http://resolver.tudelft.nl/uuid:f478913c-6565-4ba2-8537-7a5da172ef03

https://doi.org/10.1117/12.2039418

SPIE

0277-786X

http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1838231

Proceedings of SPIE 9014: Human Vision and Electronic Imaging XIX, San Francisco (USA) Febr. 02, 2014

Institutional Repository

conference paper

(c)2014 SPIE