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Reflectance Normalization in Illumination-Based Image Manipulation Detection

  • Christian RiessEmail author
  • Sven Pfaller
  • Elli Angelopoulou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9281)

Abstract

One approach to detect spliced images is to compare the lighting environment of suspicious objects or persons in the scene. The original method, proposed by Johnson and Farid, requires an investigator to mark occluding contours of multiple objects, from which the distribution of the incident light intensity is estimated. Unfortunately, this method imposes relatively strict constraints on the user and on the scene under investigation.

In this work, we propose a color-normalization approach to relax one important constraint. With our modification, a user is able to select the contours from multiple different materials (instead of having to use a single material). The proposed method will automatically compensate the differences in the reflected intensities. We demonstrate the robustness of the method with a carefully designed ground-truth dataset, consisting of 10 subjects, each of them under 3 controlled lighting environments. With the proposed method, lighting direction as a forensic cue becomes applicable to a much wider range of natural images.

Keywords

Lighting Environment Incident Light Intensity Forgery Detection Digital Imagery Image Forgery 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Christian Riess
    • 1
    Email author
  • Sven Pfaller
    • 1
  • Elli Angelopoulou
    • 1
  1. 1.Pattern Recognition LabFriedrich-Alexander UniversityErlangenGermany

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