scholarly journals Pixel Based Image Forensic Technique for Copy-move Forgery Detection Using Auto Color Correlogram

2016 ◽  
Vol 79 ◽  
pp. 383-390 ◽  
Author(s):  
Ashwini V. Malviya ◽  
Siddharth A. Ladhake
2019 ◽  
Vol 43 (2) ◽  
pp. 270-276
Author(s):  
C. Rajalakshmi ◽  
Al.M. Germanus ◽  
R. Balasubramanian

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.


2020 ◽  
Author(s):  
Jawad Khan

Proving the authenticity of images is animportant part of image forensics. Copy-move forgery is amethod of forgery commonly followed in blind image forensics.We propose the use of a modified Auto Color Correlogram toobtain feature vectors from the forged image. The featuresextracted are sent as input to a RBF-SVM that gives a score forthe possibility of a copy-move situation. We then use anormalized cross correlation for feature matching with thesame feature vectors and then produce texture attributes assmoothness and Entropy. Based on the entropy andsmoothness we use a linear regression model to classify thisand obtain a predicted score. The two outputs obtained arepassed as input to a Random forest classifier which classifiesthe image as either forged or not forged.


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