A Study on Segmentation-Based Copy-Move Forgery Detection Using DAISY Descriptor

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 398)


Copy-move forgery is one of the most prevalent forms of image forgery and a lot of methods have been developed to detect them. The most important hurdle in the development of such forgery detection methods is that different post-processing operations are done like rotation, scaling, and reflection. JPEG compression, etc., might have been applied on the copied region before it is being pasted, and the method we are using should be invariant to all types of such post-processing. Because of this, most of the methods fail in case of one or the other type of attacks. This paper presents a study on the use of segmentation-based copy-move forgery detection using rotation invariant DAISY descriptors. The paper tries to develop a new method based on three existing methods which have advantages and disadvantages of their own. The expected performance is better than any other existing methods because of the combined effect of three robust methods.


Digital image forensics Image forgery Copy-move forgery Segmentation DAISY descriptor Block-based methods Key-point-based methods 


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Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceNoorul Islam UniversityThuckalayIndia
  2. 2.Department of Information TechnologyNoorul Islam UniversityThuckalayIndia

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