Robust content authentication of gray and color images using lbp-dct markov-based features

El Sayed M. El-Alfy*, Muhammad A. Qureshi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

This paper presents a robust method for passive content authentication of gray and color images. The idea is to capture local and global artifacts resulting from the image manipulation through combining intra-block Markov features in both LBP and DCT domains. An optimized support-vector machine with radial-basis kernel is then trained to classify images as being tampered or authentic. We intensively investigate the authentication capabilities of the proposed method for separate color channels and for various combinations of them. The proposed method, without and withfeature-level fusion, is evaluated on three benchmark datasets with a variety of forgery and post-processing operations. The results show that fusing Markov features from LBP and DCT modalities leads to consistent improvement in terms of detection accuracy as compared to the state-of-the-art passive methods. Furthermore, using information from all YCbCr channels help enhancing the detection rate to more than 99.7 % on CASIA TIDE v2.0 image collection.

Original languageEnglish
Pages (from-to)14535-14556
Number of pages22
JournalMultimedia Tools and Applications
Volume76
Issue number12
DOIs
StatePublished - 1 Jun 2017

Bibliographical note

Publisher Copyright:
© 2016, Springer Science+Business Media New York.

Keywords

  • Content authentication
  • Forgery detection
  • Image manipulation
  • Local binary pattern
  • Markov-based features
  • Multimedia forensics

ASJC Scopus subject areas

  • Software
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications

Fingerprint

Dive into the research topics of 'Robust content authentication of gray and color images using lbp-dct markov-based features'. Together they form a unique fingerprint.

Cite this