Reflectance normalization in illumination-based image manipulation detection

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.