Multimedia Tools and Applications

, Volume 77, Issue 16, pp 21131–21161 | Cite as

Evaluating color and texture features for forgery localization from illuminant maps

  • Divya S. VidyadharanEmail author
  • Sabu M. Thampi


Images are widely accepted as a record of events even when images are prone to easy manipulations. It is difficult to identify image alterations by the human visual system. Once an image is identified as forged, the next step is to locate forged regions. Recently, distribution of scene illumination across an image has been analyzed to detect forged images and to locate forged image regions. In this paper, we investigate the problem of locating spliced image region based on illumination inconsistency. We investigated the discriminative power of a number of color and texture descriptors in locating spliced image regions. During digital crime investigations, often it is required to detect the spliced face in a group photo. Here, we have selected forged images containing human facial regions where the regions to be compared are of similar object material, human skin regions. We evaluated various color, texture, and combined color-texture descriptors in an unsupervised manner by comparing the distance between the feature vectors to identify the inconsistent image region. We also investigated the performance of different histogram similarity measures including heuristic histogram distance measures, non-parametric test statistics, information theoretic divergences, and cross-bin measures. Experiments show that the Local Phase Quantization (LPQ) descriptor performs best in identifying the spliced image region from the illuminant map.


Image forgery localization Illumination inconsistency Color descriptor Texture descriptor Combined color-texture descriptor Local phase quantization 



Authors acknowledge the Department of Higher Education, Government of Kerala for funding the research and the Department of Computer Science and Engineering, College of Engineering-Trivandrum for providing lab facilities to carry out the work. The authors would like to thank Dr. Tiago José De Carvalho for sharing the database. Also, authors would like to thank Mr. Aniruddha Mazumdar for sharing the source code of previous works for comparison.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Engineering-TrivandrumThiruvananthapuramIndia
  2. 2.LBS Centre for Science and TechnologyUniversity of KeralaThiruvananthapuramIndia
  3. 3.Indian Institute of Information Technology and Management-KeralaThiruvananthapuramIndia

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