Advanced video camera identification using Conditional Probability Features

Today, the misuse of digital data especially images and videos become crucial with the existence of sophisticated high-tech equipment and it is available at relatively low cost. Illegal recording of movie in cinema has caused losses of millions of dollars a year. Law enforcement agencies are keen to find ways to counter illegal video recording. Current research into camera identification techniques is attracting a significant amount of attention. The main objective is to identify the camera equipment used to record digital image or video based on the data source obtained. In this paper, we propose a video camera identification technique based on the Conditional Probability (CP) Features. Specifically we focus on its performance for identification of video sources using cameras of different models. In our experiments, we demonstrate that the CP Features are able to correctly match the test video frames with their source with classification accuracy is approximately 97.2%. These findings provide a good indication that CP Features are suitable for digital video forensics.