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

  • Venkata Udaya Sameer
  • S. Sugumaran
  • Ruchira Naskar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10063)


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.


Classification Cybercrime Digital forensics Feature extraction Feature selection Genetic Algorithm Source camera identification 


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Venkata Udaya Sameer
    • 1
  • S. Sugumaran
    • 1
  • Ruchira Naskar
    • 1
  1. 1.Department of Computer Science and EngineeringNational Institute of TechnologyRourkelaIndia

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