Structural correlation based method for image forgery classification and localization

9Citations
Citations of this article
6Readers
Mendeley users who have this article in their library.

Abstract

In the image forgery problems, previous works has been chiefly designed considering only one of two forgery types: copy-move and splicing. In this paper, we propose a scheme to handle both copy-move and splicing image forgery by concurrently classifying the image forgery types and localizing the forged regions. The structural correlations between images are employed in the forgery clustering algorithm to assemble relevant images into clusters. Then, we search for the matching of image regions inside each cluster to classify and localize tampered images. Comprehensive experiments are conducted on three datasets (MICC-600, GRIP, and CASIA 2) to demonstrate the better performance in forgery classification and localization of the proposed method in comparison with state-of-the-art methods. Further, in copy-move localization, the source and target regions are explicitly specified.

Cite

CITATION STYLE

APA

Pham, N. T., Lee, J. W., & Park, C. S. (2020). Structural correlation based method for image forgery classification and localization. Applied Sciences (Switzerland), 10(13). https://doi.org/10.3390/app10134458

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free