Digital Forensic Source Camera Identification with Efficient Feature Selection Using Filter, Wrapper and Hybrid Approaches

Digital Forensics is the branch of science dealing with investigation of evidences recovered from digital devices, to safeguard against rapidly increasing cyber crimes in today's digital world. The Source Camera Identification (SCI) problem is to map an image under question correctly to its source device. Following a Digital Forensic approach, the source of an image is detected by post-priori investigation of traces left behind in the image, by the camera. Such traces are generated due to the post-processing operations an image undergoes inside a digital camera, after being captured. In this paper, we model the SCI problem as a machine learning classification problem and focus on the most crucial component of a learning model, i.e. feature selection. We propose three different techniques for feature selection: Filter based approach, Wrapper based approach using Genetic Algorithm (GA), and also a hybrid approach with both Filter and Wrapper methods combined together. We investigate the source detection accuracy that each technique succeeds to achieve. Our experimental results suggest that the proposed methods produced a much compact feature set, hence considerably improve the source detection accuracy and minimize the training time of the learning model, as compared to the state-of-the-art.