A Novel Method for Detecting Image Sharpening Based on Local Binary Pattern

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8389)


In image forensics, determining the image editing history plays an important role as most digital images need to be edited for various purposes. Image sharpening which aims to enhance the image edge contrast for a clear view is considered to be one of the most fundamental editing techniques. However, only a few works have been reported on the detection of image sharpening. From a perspective of texture analysis, the over-shoot artifact caused by image sharpening can be regarded as a special kind of texture modification. We also find that this kind of texture modification can be characterized by local binary patterns (LBP), which is one of the most wildly used methods for texture classification. Therefore, in this paper we propose a novel method based on LBP to detect the application of sharpening in digital image. At first, we employ Canny operator for edge detection. The rotation-invariant LBP was applied to the detected edge pixels of images for feature extraction. Then features extracted from sharpened and unsharpened images are fed into a support vector machine (SVM) classifier for classification. Experimental results on digital images with different coefficients for sharpening have demonstrated the capability of this method. Comparing with the state-of-arts, the proposed method is validated to be the one with better performance in sharpening detection.


Digital forensics Texture LBP Rotation invariant Sharpen Sharpening detection 


  1. 1.
    Piva, A.: An overview on image forensics. ISRN Sig. Process. 2013, 22 (2013)Google Scholar
  2. 2.
    Lukas, J., Fridrich, J., Goljan, M.: Digital camera identification from sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 1(2), 205–214 (2006)CrossRefGoogle Scholar
  3. 3.
    Mehdi, K.L., Sencar, H.T., Memon, N.: Blind source camera identification. In: 2004 International Conference on Image Processing, ICIP’04, vol. 1 (2004)Google Scholar
  4. 4.
    Lu, C.-S., Liao, H.-Y.M.: Multipurpose watermarking for image authentication and protection. IEEE Trans. Image Process. 10(10), 1579–1592 (2001)CrossRefMATHGoogle Scholar
  5. 5.
    Gou, H., Swaminathan, A., Wu, M.: Noise features for image tampering detection and steganalysis. In: 2007 IEEE International Conference on Image Processing, ICIP 2007, vol. 6 (2007)Google Scholar
  6. 6.
    Chen, C., Yun Q.S., Wei, S.: A machine learning based scheme for double JPEG compression detection. In: 19th International Conference on Pattern Recognition, ICPR 2008 (2008)Google Scholar
  7. 7.
    Pevny, T., Fridrich, J.: Detection of double-compression in JPEG images for applications in steganography. IEEE Trans. Inf. Forensics Secur. 3(2), 247–258 (2008)CrossRefGoogle Scholar
  8. 8.
    Cao, G., Zhao, Y., Ni, R.: Detection of image sharpening based on histogram aberration and ringing artifacts. In: IEEE International Conference on Multimedia and Expo, ICME 2009 (2009)Google Scholar
  9. 9.
    Cao, G., Zhao, Y., Ni, R., Cot, A.C.: Unsharp masking sharpening detection via overshoot artifacts analysis. IEEE Signal Process. Lett. 18(10), 603–606 (2011)CrossRefGoogle Scholar
  10. 10.
    Wang, L., He, D.-C.: Texture classification using texture spectrum. Pattern Recogn. 23(8), 905–910 (1990)CrossRefGoogle Scholar
  11. 11.
    He, D.-C., Wang, L.: Texture features based on texture spectrum. Pattern Recogn. 24(5), 391–399 (1991)CrossRefGoogle Scholar
  12. 12.
    Ojala, T., Pietikainen, M., Harwood, D.: Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In: Conference A: Proceedings of the 12th IAPR International Conference on Computer Vision & Image Processing, Pattern Recognition 1994, vol. 1 (1994)Google Scholar
  13. 13.
    Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)CrossRefGoogle Scholar
  14. 14.
    Shi, Y.Q., Sutthiwan, P., Chen, L.: Textural features for steganalysis. In: Kirchner, M., Ghosal, D. (eds.) IH 2012. LNCS, vol. 7692, pp. 63–77. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  15. 15.
    Li, Z., Ye, J., Shi, Y.: Distinguishing computer graphics from photo-graphic images using local binary patterns. In: Proceeding of the 11th International Workshop on Digital-forensics and Watermarking (2012)Google Scholar
  16. 16.
    Xu, G., Shi, Y.Q.: Camera model identification using local binary patterns. In: IEEE International Conference on Multimedia and Expo (ICME) (2012)Google Scholar
  17. 17.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefGoogle Scholar
  18. 18.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall, Upper Saddle River (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Electrical and Computer EngineeringNew Jersey Institute of TechnologyNewarkUSA
  2. 2.Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina

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