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Volume: 29 | Article ID: art00013
Image Recapture Detection with Convolutional and Recurrent Neural Networks
  DOI :  10.2352/ISSN.2470-1173.2017.7.MWSF-329  Published OnlineJanuary 2017

In this paper, we aim to address the image recapturing detection problem with the convolutional and recurrent neural networks. With the advances of image display and acquisition techniques, the recaptured images are of satisfactory quality nowadays. This has been creating an ever stronger demand for sophisticated image recapturing detection algorithms which can efficiently prevent the unauthorized image distributing and forging. In this paper, we propose a hierarchical feature learning strategy by leveraging the intra-block information and inter-block dependency for image recapturing detection. In particular, the image blocks are first employed as the input of the convolutional neural network (CNN) and subsequently recurrent neural network (RNN) is further adopted to extract block dependencies. The CNN and RNN serve as effective tools to extract discriminative and meaningful features regarding both intra- and inter-block information. As such, the inherent properties within local blocks and the correlations between non-local neighbouring blocks are all exploited to identify the recaptured images. Experimental results on three databases show that significantly better performance can be achieved with our proposed framework compared to traditional handcrafted and deep learning based approaches.

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Haoliang Li, Shiqi Wang, Alex C. Kot, "Image Recapture Detection with Convolutional and Recurrent Neural Networksin Proc. IS&T Int’l. Symp. on Electronic Imaging: Media Watermarking, Security, and Forensics,  2017,  pp 87 - 91,

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