Copy move forgery detection using key point localized super pixel based on texture features
Rajalakshmi C., Germanus Al.M., Balasubramanian R.

Research scholar Roll No:12332, Dept. of Computer Science, Manonmaniam Sundaranar University, Abishekapatti,Tirunelveli 627012,Tamil Nadu, India,
Dept. of Computer Science, Kamarajar Government Arts College, Surandai,

Dept. of Computer Science & Engg., Manonmaniam Sundaranar University, Abishekapatti,Tirunelveli

The most important barrier in the image forensic is to ensue a forgery detection method such can detect the copied region which sustains rotation, scaling reflection, compressing or all. Traditional SIFT method is not good enough to yield good result. Matching accuracy is not good. In order to improve the accuracy in copy move forgery detection, this paper suggests a forgery detection method especially for copy move attack using Key Point Localized Super Pixel (KLSP). The proposed approach harmonizes both Super Pixel Segmentation using Lazy Random Walk (LRW) and Scale Invariant Feature Transform (SIFT) based key point extraction. The experimental result indicates the proposed KLSP approach achieves better performance than the previous well known approaches.

copy move, segmentation, SIFT, KLSP.

Rajalakshmi C, Alex MG, Balasubramanian R. Copy move forgery detection using key point localized super pixel based on texture features. Computer Optics 2019; 43(2): 270-276. DOI: 10.18287/2412-6179-2019-43-2-270-276.


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