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Recapture Image Forensics Based on Laplacian Convolutional Neural Networks

  • Pengpeng Yang
  • Rongrong NiEmail author
  • Yao Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10082)

Abstract

Recapture image forensics has drawn much attention in public security forensics. Although some algorithms have been proposed to deal with it, there is still great challenge for small-size images. In this paper, we propose a generalized model for small-size recapture image forensics based on Laplacian Convolutional Neural Networks. Different from other Convolutional Neural Networks models, We put signal enhancement layer into Convolutional Neural Networks structure and Laplacian filter is used in the signal enhancement layer. We test the proposed method on four kinds of small-size image databases. The experimental results have demonstrate that the proposed algorithm is effective. The detection accuracies for different image size database are all above 95%.

Keywords

Recapture images forensics Laplacian Convolution Neural Networks Laplacian filter 

Notes

Acknowledgments

This work was supported in part by National NSF of China (61332012, 61272355, 61672090), Fundamental Research Funds for the Central Universities (2015JBZ002), the PAPD, the CICAEET. We greatly acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Beijing Key Laboratory of Advanced Information Science and Network Technology, Institute of Information ScienceBeijing Jiaotong UniversityBeijingChina

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