Source Camera Identification and Detection in Digital Videos through Blind Forensics

Abstract: Source camera identification in digital videos is the problem of associating an unknown digital video with its source device, within a closed set of possible devices. The existing techniques in source detection of digital videos try to find a fingerprint of the actual source in the video in form of PRNU (Photo Response Non--Uniformity), and match it against the SPN (Sensor Pattern Noise) of each possible device. The highest correlation indicates the correct source. We investigate the problem of identifying a video source through a feature based approach using machine learning. In this paper, we present a blind forensic technique of video source authentication and identification, based on feature extraction, feature selection and subsequent source classification. The main aim is to determine whether a claimed source for a video is actually its original source. If not, we identify its original source. Our experimental results prove the efficiency of the proposed method compared to traditional fingerprint based technique.