Eliminating the effects of illumination condition in feature based camera model identification

State-of-the-art techniques for Camera Model Identification operate by extracting different features from the training image set and incorporating those features to predict the source of test images using machine learning. Though the existing approaches perform efficiently for images captured in natural daylight or bright illumination conditions, the state-of-the-art lacks sufficient experiments and results to evaluate efficiency of such schemes for images captured in dark illumination conditions. In this paper, we present a set of experiments to assess the impact of illumination conditions, on image source classification problem, and also propose an image filtering based technique to eliminate the adverse effects of scene illumination on source classification accuracy. Our experimental results prove that the performance efficiency of existing feature based source classification techniques, is indeed dependent on the illumination conditions. The proposed strategy enables our source classification model to achieve high efficiency as compared to the state-of-the-art, under all illumination conditions.