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)


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%.


Recapture images forensics Laplacian Convolution Neural Networks Laplacian filter 



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.


  1. 1.
    Yu, H., Ng, T.T., Sun, Q.: Recapture photo detection using specularity distribution. In: 15th IEEE International Conference on Image Processing, pp. 3140–3143 (2008)Google Scholar
  2. 2.
    Gao, X., Ng, T.T., Qiu, B., Chang, S.F.: Single-view recaptured image detection based on physics-based features. In: 2010 IEEE International Conference on Multimedia and Expo (ICME), pp. 1469–1474. IEEE (2010)Google Scholar
  3. 3.
    Cao, H., Alex, K.C.: Identification of recaptured photographs on LCD screens. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1790–1793 (2010)Google Scholar
  4. 4.
    Li, R., Ni, R., Zhao, Y.: An effective detection method based on physical traits of recaptured images on LCD screens. In: Shi, Y.-Q., Kim, H.J., Pérez-González, F., Echizen, I. (eds.) IWDW 2015. LNCS, vol. 9569, pp. 107–116. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-31960-5_10 CrossRefGoogle Scholar
  5. 5.
    Chen, J., Kang, X., Liu, Y., Wang, Z.J.: Median filtering forensics based on convolutional neural networks. IEEE Signal Process. Lett. 22(11), 1849–1853 (2015)CrossRefGoogle Scholar
  6. 6.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  7. 7.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385 (2015)
  8. 8.
    An open source framework of deep learning.

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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