An Effective Image Detection Algorithm for USM Sharpening Based on Pixel-Pair Histogram

  • Hang Gao
  • Mengting Hu
  • Tiegang GaoEmail author
  • Renhong Cheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11165)


USM sharpening is a popular method for enhancement of image quality, detection of image sharpening has attracted much attention in recent years. A novel image sharpening detection algorithm is proposed in this paper. In the scheme, different from some image forensic schemes, which used Cb or Cr channel of YCbCr color model to extract image features for forensics, in this paper, color images are firstly transformed into the YCbCr model, then the luminance channel of YCbCr color model is selected to extract pixel-pair histogram features based on four directional differential matrixes, these features within some threshold scope constitute the final image features. LIBSVM is used to implement classification for real and sharpened image. Widely used UCID database is employed to conduct test with various sharpening strength and range. Experimental results show that the proposed algorithm has superior performance; extensive comparisons with some existing algorithms show that it outperforms state-of-art methods investigated, even if the sharpening intensity is very weak (σ = 0.3).


USM sharpening Pixel-pair histogram Image forensic 



The work was supported by the Program of Natural Science Fund of Tianjin, China (Grant No. 16JCYBJC15700).


  1. 1.
    Cao, Y., Gao, T., Yang, Q.: A robust detection algorithm for copy-move forgery in digital images. Forensic Sci. Int. 214(1–3), 33–43 (2012)CrossRefGoogle Scholar
  2. 2.
    Fadl, S.M., Semary, N.A.: Robust copy-move forgery revealing in digital images using polar coordinate system. Neurocomputing 265, 57–65 (2017)CrossRefGoogle Scholar
  3. 3.
    Zhao, X., Wang, S., Li, S., Li, J.: Passive image-splicing detection by a 2-D noncausal Markov model. IEEE Trans. Circuits Syst. Video Technol. 25(2), 185–199 (2015)CrossRefGoogle Scholar
  4. 4.
    He, P., Jiang, X., Sun, T., Wang, S.: Detection of double compression in MPEG-4 videos based on block artifact measurement. Neurocomputing 228, 84–96 (2017)CrossRefGoogle Scholar
  5. 5.
    Yang, J., Xie, J., Zhu, G., Kwong, S., Shi, Y.: An effective method for detecting double JPEG compression with the same quantization matrix. IEEE Trans. Inf. Forensics Secur. 9(11), 1933–1942 (2014)CrossRefGoogle Scholar
  6. 6.
    Cao, G., Zhao, Y., Ni, R., Li, X.: Contrast enhancement-based forensics in digital images. IEEE Trans. Inf. Forensics Secur. 9, 515–525 (2014)CrossRefGoogle Scholar
  7. 7.
    Yang, L., Gao, T., Xuan, Y., Gao, H.: Contrast modification forensics algorithm based on merged weight histogram of run length. Int. J. Digit. Crime Forensics 8(2), 27–35 (2016)CrossRefGoogle Scholar
  8. 8.
    Kang, X., Stamm, M.C., Peng, A., Liu, K.J.R.: Robust median filtering forensics using an autoregressive model. IEEE Trans. Inf. Forensics Secur. 8, 1456–1468 (2013)CrossRefGoogle Scholar
  9. 9.
    Chen, J., Kang, X., Liu, Y., Wang, Z.J.: Median filtering forensics based on convolutional neural networks. IEEE Signal Process. Lett. 22, 1849–1853 (2015)CrossRefGoogle Scholar
  10. 10.
    Wang, Q., Zhang, R.: Double JPEG compression forensics based on a convolutional neural network. EURASIP J. Inf. Secur. 2016(1), 23 (2016)CrossRefGoogle Scholar
  11. 11.
    Barni, M., Bondi, L., Bonettini, N., et al.: Aligned and non-aligned double JPEG detection using convolutional neural networks. J. Vis. Commun. Image Represent. 49, 153–163 (2017)CrossRefGoogle Scholar
  12. 12.
    Salloum, R., Ren, Y., Jay Kuo, C.-C.: Image splicing localization using a multi-task fully convolutional network (MFCN). J. Vis. Commun. Image Represent. 51, 201–209 (2018)CrossRefGoogle Scholar
  13. 13.
    Sun, J.-Y., Kim, S.-W., Lee, S.-W., Ko, S.-J.: A novel contrast enhancement forensics based on convolutional neural networks. Signal Process. Image Commun. 63, 149–160 (2018)CrossRefGoogle Scholar
  14. 14.
    Cao, G., Zhao, Y., Ni, R.: Detection of image sharpening based on histogram aberration and ringing artifacts. In: Proceedings of IEEE International Conference on Multimedia and Expo (ICME), pp. 1026–1029 (2009)Google Scholar
  15. 15.
    Cao, G., Zhao, Y., Ni, R., et al.: Unsharp masking sharpening detection via overshoot artifacts analysis. IEEE Signal Process. Lett. 18(10), 603–606 (2011)CrossRefGoogle Scholar
  16. 16.
    Ding, F., Zhu, G., Shi, Y.Q.: A novel method for detecting image sharpening based on local binary pattern. In: Shi, Y.Q., Kim, H.-J., Pérez-González, F. (eds.) IWDW 2013. LNCS, vol. 8389, pp. 180–191. Springer, Heidelberg (2014). Scholar
  17. 17.
    Ding, F., Zhu, G., Yang, J., et al.: Edge perpendicular binary coding for USM sharpening detection. IEEE Signal Process. Lett. 22(3), 327–331 (2015)CrossRefGoogle Scholar
  18. 18.
    Gu, Y., Wang, S., Lin, X., Sun, T.: USM sharpening detection based on sparse coding. In: Proceedings of 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–5 (2016) Google Scholar
  19. 19.
    Ding, F., Zhu, G., Dong, W., Shi, Y.: An efficient weak sharpening detection method for image forensics. J. Vis. Commun. Image Represent. 50, 93–99 (2018)CrossRefGoogle Scholar
  20. 20.
    Hussain, M., Saleh, S.Q., Bebis, G., Muhammad, G., Aboalsamh, H.: Evaluation of image forgery detection using multi-scale weber local descriptors. Int. J. Artif. Intell. Tools 24(4), 1–27 (2015)CrossRefGoogle Scholar
  21. 21.
    Shabanifard M., Shayesteh M.G., Akhaee M.A.: Forensic detection of image manipulation using the Zernike moments and pixel-pair histogram. IET Image Process. 7(9), 817–828 (2013)CrossRefGoogle Scholar
  22. 22.
    Schaefer, G., Stich, M.: UCID—an uncompressed colour image database. Storage Retr. Methods Appl. Multimed. 5307, 472–480 (2003)Google Scholar
  23. 23.

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Hang Gao
    • 1
  • Mengting Hu
    • 1
  • Tiegang Gao
    • 2
    Email author
  • Renhong Cheng
    • 1
  1. 1.College of Computer and Control EngineeringNankai UniversityTianjinChina
  2. 2.College of SoftwareNankai UniversityTianjinChina

Personalised recommendations