DocumentCode :
19047
Title :
An Image Recapture Detection Algorithm Based on Learning Dictionaries of Edge Profiles
Author :
Thongkamwitoon, Thirapiroon ; Muammar, Hani ; Dragotti, Pier-Luigi
Author_Institution :
Dept. of Electr. & Electron. Eng., Imperial Coll. London, London, UK
Volume :
10
Issue :
5
fYear :
2015
fDate :
May-15
Firstpage :
953
Lastpage :
968
Abstract :
With today´s digital camera technology, high-quality images can be recaptured from an liquid crystal display (LCD) monitor screen with relative ease. An attacker may choose to recapture a forged image in order to conceal imperfections and to increase its authenticity. In this paper, we address the problem of detecting images recaptured from LCD monitors. We provide a comprehensive overview of the traces found in recaptured images, and we argue that aliasing and blurriness are the least scene dependent features. We then show how aliasing can be eliminated by setting the capture parameters to predetermined values. Driven by this finding, we propose a recapture detection algorithm based on learned edge blurriness. Two sets of dictionaries are trained using the K-singular value decomposition approach from the line spread profiles of selected edges from single captured and recaptured images. An support vector machine classifier is then built using dictionary approximation errors and the mean edge spread width from the training images. The algorithm, which requires no user intervention, was tested on a database that included more than 2500 high-quality recaptured images. Our results show that our method achieves a performance rate that exceeds 99% for recaptured images and 94% for single captured images.
Keywords :
edge detection; image classification; singular value decomposition; support vector machines; K-singular value decomposition approach; LCD monitors; aliasing elimination; dictionary approximation errors; edge profiles; image recapture detection algorithm; learned edge blurriness; learning dictionaries; line spread profiles; mean edge spread width; support vector machine classifier; Cameras; Feature extraction; Image color analysis; Image sensors; Lenses; Monitoring; Noise; Image forensics; K-SVD; aliasing; blurriness; dictionary learning; image acquisition; recapture detection;
fLanguage :
English
Journal_Title :
Information Forensics and Security, IEEE Transactions on
Publisher :
ieee
ISSN :
1556-6013
Type :
jour
DOI :
10.1109/TIFS.2015.2392566
Filename :
7010054
Link To Document :
بازگشت