Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers
Author:
Affiliation:
1. University College of Engineering, JNTU Kakinada,Kakinada,India,533003
2. JNTUK UCE,Department of ECE,Vizianagaram,India,535003
Publisher
Tsinghua University Press
Subject
Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Information Systems
Link
http://xplorestaging.ieee.org/ielx7/8254253/10097649/10097650.pdf?arnumber=10097650
Reference49 articles.
1. Image forgery detection based on the fusion of machine learning and block-matching methods;cozzolino;ArXiv Preprint,2013
2. An optimized technique for copy–move forgery localization using statistical features
3. Image authentication using local binary pattern on the low frequency components;rao;Lecture Notes in Electrical Engineering,2016
4. Fractional Quaternion Zernike Moments for Robust Color Image Copy-Move Forgery Detection
5. Copy-Move Forgery Detection: A State-of-the-Art Technical Review and Analysis
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