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Markov Feature Extraction Using Enhanced Threshold Method for Image Splicing Forgery Detection

  • Avinash Kumar
  • Choudhary Shyam Prakash
  • Sushila Maheshkar
  • Vikas Maheshkar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 670)

Abstract

Use of sophisticated image editing tools and computer graphics makes easy to edit, transform, or eliminate the significant features of an image without leaving any prominent proof of tampering. One of the most commonly used tampering techniques is image splicing. In image splicing, a portion of image is cut and paste it on the same image or different image to generate a new tampered image, which is hardly noticeable by naked eyes. In the proposed method, enhanced Markov model is applied in the block discrete cosine transform (BDCT) domain as well as in discrete Meyer wavelet transform (DMWT) domain. To classify the spliced image from an authentic image, the cross-domain features play the role of final discriminative features for support vector machine (SVM) classifier. The performance of the proposed method through experiments is estimated on the publicly available dataset (Columbia dataset) for image splicing. The experimental results show that the proposed method performs better than some of the existing state of the art.

Keywords

Image forensics Image splicing Markov feature DMWT SVM 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Avinash Kumar
    • 1
  • Choudhary Shyam Prakash
    • 1
  • Sushila Maheshkar
    • 2
  • Vikas Maheshkar
    • 1
    • 3
  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology (Indian School of Mines)DhanbadIndia
  2. 2.Department of Computer Science and EngineeringNational Institute of Technology DelhiDelhiIndia
  3. 3.Division of Information TechnologyNetaji Subhas Institute of TechnologyNew DelhiIndia

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