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Recaptured image detection based on convolutional neural networks with local binary patterns coding
Author(s): Nan Zhu; Minying Qin; Yuting Yin
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Paper Abstract

With the great development of image display technology and the widespread use of various image acquisition device, recapturing high-quality images from high-fidelity LCD (liquid crystal display) screens becomes relative convenient. These recaptured images pose serious threats on image forensic technologies and bio-authentication systems. In order to prevent the security loophole of image recapture attack, inspired by the effectiveness of LBP (local binary pattern) on recaptured image detection and the satisfactory performance of deep learning techniques on many image forensics tasks, we propose a recaptured image detection method based on convolutional neural networks with local binary patterns coding. The LBP coded maps are extracted as the input of the proposed convolutional neural networks architecture. Extensive experiments on two public high-quality recaptured image databases under two different scenarios demonstrate the superior of our designed method when compared with the state-of-the-art approaches.

Paper Details

Date Published: 31 July 2019
PDF: 6 pages
Proc. SPIE 11198, Fourth International Workshop on Pattern Recognition, 1119804 (31 July 2019); doi: 10.1117/12.2540496
Show Author Affiliations
Nan Zhu, Xi'an Technological Univ. (China)
Minying Qin, Xi'an Technological Univ. (China)
Yuting Yin, Xi'an Technological Univ. (China)


Published in SPIE Proceedings Vol. 11198:
Fourth International Workshop on Pattern Recognition
Xudong Jiang; Zhenxiang Chen; Guojian Chen, Editor(s)

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