An Effective Detection Method Based on Physical Traits of Recaptured Images on LCD Screens

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9569)

Abstract

The detection of recaptured images plays a particular role in public security forensics. Although researches achieve some progress, low quality of image samples and long time consuming for feature extraction are still prominent problems. From the analysis to the photography process, we present two effective features for distinguishing high-resolution and high-quality recaptured images from LCD screens. One feature is the block effect and blurriness effect caused by JPEG compression, and the other feature is screen effect described by wavelet decomposition with aliasing-enhancement preprocessing. Experiments show that the proposed scheme obtains outstanding performances, which is fast and has higher discriminative accuracy.

Keywords

Recaptured images Block effect Blurriness effect Aliasing-enhancement preprocessing Wavelet decomposition 

Notes

Acknowledgement

This work was supported in part by 973 Program (2011CB302204), National NSF of China (61332012, 61272355), PCSIRT (IRT 201206), Fundamental Research Funds for the Central Universities (2015JBZ002), Open Projects Program of NLPR (201306309).

References

  1. 1.
    Yu, H., Ng, T.T., Sun, Q.: Recaptured photo detection using specularity distribution. In: 15th IEEE International Conference on ICIP 2008. IEEE, pp. 3140–3143 (2008)Google Scholar
  2. 2.
    Cao, H., Alex, K.: Identification of recaptured photographs on LCD screens. In: 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 1790–1793. IEEE (2010)Google Scholar
  3. 3.
    Yin, J., Fang, Y.M.: Digital image forensics for photographic copying. In: IS&T/SPIE Electronic Imaging. International Society for Optics and Photonics, pp. 83030F–83030F-7 (2012)Google Scholar
  4. 4.
    Thongkamwitoon, T., Muammar, H., Dragotti, P.L.: An image recapture detection algorithm based on learning dictionaries of edge profiles. IEEE Trans. Inf. Forensics Secur. 953–968 (2015)Google Scholar
  5. 5.
    Zhou, W., Hamid, R.S., Alan, C.B.: No-reference perceptual quality assessment of JPEG compressed images. 2002 International Conference on Image Processing, Proceedings, vol. 1, pp. I-477–I-480. IEEE (2002)Google Scholar
  6. 6.
    Tan, X.Y., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 168–182 (2010)Google Scholar
  7. 7.
    Lyu, S., Farid, H.: How realistic is photorealistic? IEEE Trans. Signal Process. 53(2), 845–850 (2005)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Chang, C.C., Lin, C.J.: LIBSVM - a library for support vector machines. http://www.csie.ntu.edu.tw/cjlin/libsvm

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of Information Science and Beijing Key Laboratory of Advanced Information Science and Network TechnologyBeijing Jiaotong UniversityBeijing China

Personalised recommendations