Hybrid G-PRNU: Optimal parameter selection for scale-invariant asymmetric source smartphone identification

The ease in counterfeiting both origin and content of a video necessitates the search for a reliable method to identify the source of a media file a crucial part of forensic investigation. One of the most accepted solutions to identify the source of a digital image involves comparison of its photo-response non-uniformity (PRNU) fingerprint. However, for videos, prevalent methods are not as efficient as image source identification techniques. This is due to the fact that the fingerprint is affected by the postprocessing steps done to generate the video. In this paper, we answer affirmatively to the question of whether one can use images to generate the reference fingerprint pattern to identify a video source. We introduce an approach called “Hybrid G-PRNU” that provides a scale-invariant solution for video source identification by matching its fingerprint with the one extracted from images. Another goal of our work is to find the optimal parameters to reach an optimal identification rate. Experiments performed demonstrate higher identification rate, while doing asymmetric comparison of video PRNU with the reference pattern generated from images, over several test cases. Further the fingerprint extractor used for this paper is being made freely available for scholars and researchers in the domain.