Multimedia Tools and Applications

, Volume 76, Issue 12, pp 14535–14556 | Cite as

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

  • El-Sayed M. El-Alfy
  • Muhammad A. Qureshi


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.


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



Credits for the use of the datasets are given to DVMM Laboratory of Columbia University, CalPhotos Digital Library and the photographers listed therein, and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science, Corel Image Database and the photographers therein. The authors would like also to acknowledge the support provided by the Deanship of Scientific Research (DSR) at King Fahd University of Petroleum & Minerals (KFUPM) for funding this work through the Intelligent Systems Research Group (ISRG) under project No. RG1113-1&2.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Information and Computer Science DepartmentKing Fahd University of Petroleum and MineralsDhahranSaudi Arabia
  2. 2.Electrical Engineering DepartmentKing Fahd University of Petroleum and MineralsDhahranSaudi Arabia

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