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Copy-Move Forgery Localization Using Convolutional Neural Networks and CFA Features
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Author(s): Lu Liu (Institute of Information Science, Beijing Jiaotong University, Beijing, China), Yao Zhao (Institute of Information Science, Beijing Jiaotong University, Beijing, China), Rongrong Ni (Institute of Information Science, Beijing Jiaotong University, Beijing, China)and Qi Tian (Department of Computer Science, University of Texas at San Antonio, San Antonio, US)
Copyright: 2020
Pages: 16
Source title:
Cyber Warfare and Terrorism: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-2466-4.ch081
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Abstract
This article describes how images could be forged using different techniques, and the most common forgery is copy-move forgery, in which a part of an image is duplicated and placed elsewhere in the same image. This article describes a convolutional neural network (CNN)-based method to accurately localize the tampered regions, which combines color filter array (CFA) features. The CFA interpolation algorithm introduces the correlation and consistency among the pixels, which can be easily destroyed by most image processing operations. The proposed CNN method can effectively distinguish the traces caused by copy-move forgeries and some post-processing operations. Additionally, it can utilize the classification result to guide the feature extraction, which can enhance the robustness of the learned features. This article, per the authors, tests the proposed method in several experiments. The results demonstrate the efficiency of the method on different forgeries and quantifies its robustness and sensitivity.
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