Source Camera Identification and Detection in Digital Videos through Blind Forensics

TL;DR

This paper introduces a machine learning-based blind forensic method for source camera identification in digital videos, focusing on feature extraction and classification to verify or find the original source.

Contribution

It proposes a novel feature-based approach for video source identification that improves over traditional fingerprint matching techniques.

Findings

Proves the efficiency of the proposed method

Outperforms traditional fingerprint-based techniques

Effective in verifying or identifying original video sources

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.