Recaptured image detection based on convolutional neural networks with local binary patterns coding

Nan Zhu; Minying Qin; Yuting Yin

doi:10.1117/12.2540496

31 July 2019 Recaptured image detection based on convolutional neural networks with local binary patterns coding

Nan Zhu, Minying Qin, Yuting Yin

Proceedings Volume 11198, Fourth International Workshop on Pattern Recognition; 1119804 (2019) https://doi.org/10.1117/12.2540496
Event: Fourth International Workshop on Pattern Recognition, 2019, Nanjing, China

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.

Citation Download Citation

Nan Zhu, Minying Qin, and Yuting Yin "Recaptured image detection based on convolutional neural networks with local binary patterns coding", Proc. SPIE 11198, Fourth International Workshop on Pattern Recognition, 1119804 (31 July 2019); https://doi.org/10.1117/12.2540496

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