Recaptured Image Forensics Based on Quality Aware and Histogram Feature

  • Pengpeng Yang
  • Ruihan Li
  • Rongrong NiEmail author
  • Yao Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10431)


The recaptured images forensics has drawn much attention in forensics community. The technology can provide some evidences for copyright protection and protect the face spoofing system to a certain degree. In this paper, we propose an algorithm to detect the images recaptured from LCD screen. On the one hand, the quality of the recaptured images would be affect in general. The generalized Gaussian distribution (GGD) and zero mode asymmetric generalized Gaussian distribution (AGGD) effectively capture the behavior of the coefficients of natural and distorted versions of them. So the parameters of GGD with zero mean and zero mode AGGD are estimated as the quality aware feature. On the other hand, the correlation of DCT coefficients between two adjacent positions would be changed. The histogram feature of difference matrix of DCT coefficients is used to measure it. The experimental results show that the proposed method obtains a outstanding detection accuracy.


Recaptured image forensics Quality aware features DCT coefficient 



This work was supported in part by National NSF of China (61672090, 61332012), the National key research and development program of China (2016YFB0800404), Fundamental Research Funds for the Central Universities (2015JBZ002).


  1. 1.
    Piva, A.: An overview on image forensics. ISRN Sig. Process. 2013, 22 (2013)Google Scholar
  2. 2.
    Lyu, S., Farid, H.: How realistic is photorealistic? IEEE Trans. Sig. Process. 53(2), 845–850 (2005)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Cao, H., Kot, A.C.: Identification of recaptured photographs on LCD screens. In: 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE (2010)Google Scholar
  4. 4.
    Yu, H., Ng, T.-T., Sun, Q.: Recaptured photo detection using specularity distribution. In: 2008 15th IEEE International Conference on Image Processing. IEEE (2008)Google Scholar
  5. 5.
    Gao, X., et al.: Single-view recaptured image detection based on physics-based features. In: 2010 IEEE International Conference on Multimedia and Expo (ICME). IEEE (2010)Google Scholar
  6. 6.
    Zhai, X., Ni, R., Zhao, Y.: Recaptured image detection based on texture features. In: 2013 Ninth International Conference on Intelligent Information Hiding and Multimedia Signal Processing. IEEE (2013)Google Scholar
  7. 7.
    Ni, R., Zhao, Y., Zhai, X.: Recaptured images forensics based on color moments and DCT coefficients features (2015)Google Scholar
  8. 8.
    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, Cham (2016). doi: 10.1007/978-3-319-31960-5_10 CrossRefGoogle Scholar
  9. 9.
    Yang, P., Ni, R., Zhao, Y.: Recapture image forensics based on Laplacian convolutional neural networks. In: Shi, Y.Q., Kim, H.J., Perez-Gonzalez, F., Liu, F. (eds.) IWDW 2016. LNCS, vol. 10082, pp. 119–128. Springer, Cham (2017). doi: 10.1007/978-3-319-53465-7_9 CrossRefGoogle Scholar
  10. 10.
    Li, H., Wang, S., Kot, A.C.: Image recapturing detection with convolutional and recurrent neural network. In: Proceedings of IS&T, Electronic Imaging, Media Watermarking, Security, and Forensics (2017)Google Scholar
  11. 11.
    Mittal, A., Soundararajan, R., Bovik, A.C.: Making a completely blind image quality analyzer. IEEE Sig. Process. Lett. 20(3), 209–212 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Pengpeng Yang
    • 1
  • Ruihan Li
    • 1
  • Rongrong Ni
    • 1
    Email author
  • Yao Zhao
    • 1
  1. 1.Beijing Key Laboratory of Advanced Information Science and Network Technology, Institute of Information ScienceBeijing Jiaotong UniversityBeijingChina

Personalised recommendations