An efficient copy move forgery detection using deep learning feature extraction and matching algorithm

The image forgery activities are on the rise because of the development of various image editing tools. Such activities are done by attackers with intentions of defaming people and websites or for gaining monetary advantage, extortion etc. Image forgeries are carried out through various ways, among...

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Veröffentlicht in:Multimedia tools and applications Jg. 79; H. 11-12; S. 7355 - 7376
Hauptverfasser: Agarwal, Ritu, Verma, Om Prakash
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.03.2020
Springer Nature B.V
Schlagworte:
Algorithms
Computer Communication Networks
Computer Science
Computing time
Copying
Data Structures and Information Theory
Deep learning
Feature extraction
Forgery
Image contrast
Image detection
Image segmentation
Machine learning
Multimedia Information Systems
Special Purpose and Application-Based Systems
Websites
Copy-move detection
Tampered image identification
Digital image forgery
ISSN:1380-7501, 1573-7721
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Zusammenfassung:The image forgery activities are on the rise because of the development of various image editing tools. Such activities are done by attackers with intentions of defaming people and websites or for gaining monetary advantage, extortion etc. Image forgeries are carried out through various ways, among one is the copy-move forgery. The basic process of copy-move image forgery is copying the objects present in an image and create the new image by using the copied objects or placing the copied object on the same image on a different location, hence the need for a forgery detection system to protect the authenticity of images. The existing forgery detection techniques detect the tampered regions with less efficiency because of the large size and lower contrast of the images. This article proposes an efficient technique for detecting the copy-move forged image based on deep learning. The proposed algorithm initializes the tampered image as the input for our system to detect the tampered region. Our system includes processes like segmentation, feature extraction, dense depth reconstruction, and finally identifying the tampered areas. The proposed deep learning based system can save on computational time and detect the duplicated regions with more accuracy.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-019-08495-z