Journal
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
Volume 11, Issue 4, Pages 720-733Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIFS.2015.2506548
Keywords
Digital forensics; splicing detection; illuminant maps; image descriptors; machine learning; diversity measures
Funding
- Coordination for the Improvement of Higher Education Personnel [0214-13-2]
- Microsoft Research
- CAPES DeepEyes Project
- Sao Paulo Research Foundation [2010/05647-4, 2010/14910-0, 2011/22749-8]
- Brazilian National Research Council [140916/2012-1, 477662/2013-7, 307113/2012-4, 304352/2012-8]
- Instituto Federal de Educacao, Ciencia e Tecnologia do Sudeste de Minas Gerais
- University of Campinas
- Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) [10/14910-0, 10/05647-4] Funding Source: FAPESP
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In this paper, we explore transformed spaces, represented by image illuminant maps, to propose a methodology for selecting complementary forms of characterizing visual properties for an effective and automated detection of image forgeries. We combine statistical telltales provided by different image descriptors that explore color, shape, and texture features. We focus on detecting image forgeries containing people and present a method for locating the forgery, specifically, the face of a person in an image. Experiments performed on three different open-access data sets show the potential of the proposed method for pinpointing image forgeries containing people. In the two first data sets (DSO-1 and DSI-1), the proposed method achieved a classification accuracy of 94% and 84%, respectively, a remarkable improvement when compared with the state-of-the-art methods. Finally, when evaluating the third data set comprising questioned images downloaded from the Internet, we also present a detailed analysis of target images.
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