Welcome to the InfoSci Platform
Reference Hub5
Copy-Move Forgery Localization Using Convolutional Neural Networks and CFA Features

Copy-Move Forgery Localization Using Convolutional Neural Networks and CFA Features

Lu Liu, Yao Zhao, Rongrong Ni, Qi Tian
Copyright: © 2018 |Volume: 10 |Issue: 4 |Pages: 16
ISSN: 1941-6210|EISSN: 1941-6229|EISBN13: 9781522543633|DOI: 10.4018/IJDCF.2018100110
Cite Article Cite Article

MLA

Liu, Lu, et al. "Copy-Move Forgery Localization Using Convolutional Neural Networks and CFA Features." IJDCF vol.10, no.4 2018: pp.140-155. http://doi.org/10.4018/IJDCF.2018100110

APA

Liu, L., Zhao, Y., Ni, R., & Tian, Q. (2018). Copy-Move Forgery Localization Using Convolutional Neural Networks and CFA Features. International Journal of Digital Crime and Forensics (IJDCF), 10(4), 140-155. http://doi.org/10.4018/IJDCF.2018100110

Chicago

Liu, Lu, et al. "Copy-Move Forgery Localization Using Convolutional Neural Networks and CFA Features," International Journal of Digital Crime and Forensics (IJDCF) 10, no.4: 140-155. http://doi.org/10.4018/IJDCF.2018100110

Export Reference

Mendeley
Favorite Full-Issue Download

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.