Image Splicing Forgery Detection Scheme Using New Local Binary Pattern Varient


  • Araz R. Abrahim Faculty of Computing, Universiti Teknologi Malaysia (UTM), Skudai, 81310, Johor Bahru, Malaysia
  • Mohd Sh. Mohd Rahim IRDA Digital Media Center, Universiti Teknologi Malaysia (UTM), Skudai, 81310, Johor Bahru, Malaysia
  • Ahmed S. Sami Faculty of Computer Science, Duhok Polytechnic Unicersity, Duhok, Kurdistan Region of Iraq



In this research develop passive image splicing detection method based on a new descriptor called Adaptive Threshold Mean Ternary Pattern (ATMTP). It was developed based on strength and weaknesses of both Local Binary Pattern (LBP) and Local Ternary Pattern (LTP). ATMTP extraction feature is normally achieved by using proposed mean based thresholding and adaptive ternary thresholding, the former is robust to noise while the latter is robust to noise and other photometric attacks. It is designed to withstand against photometric manipulations, be it single or double attacks. In this research the ATMTP color features extracted from R, G, and B channels have revealed that the present method achieved higher accuracy on standard datasets CASIA V2.0 out of 99.03%, Sensitivity 99.6%, and specificity 98.1%. Finally, in terms of accuracy, the proposed SFD scheme outperformed the best recent works in this area.


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How to Cite

Abrahim, A. R., Mohd Rahim, M. S., & Sami, A. S. (2020). Image Splicing Forgery Detection Scheme Using New Local Binary Pattern Varient. Academic Journal of Nawroz University, 9(3), 208–215.