Abstract
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.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Piva, A.: An overview on image forensics. ISRN Sig. Process. 2013, 22 (2013)
Lukas, J., Fridrich, J., Goljan, M.: Digital camera identification from sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 1(2), 205–214 (2006)
Mehdi, K.L., Sencar, H.T., Memon, N.: Blind source camera identification. In: 2004 International Conference on Image Processing, ICIP’04, vol. 1 (2004)
Lu, C.-S., Liao, H.-Y.M.: Multipurpose watermarking for image authentication and protection. IEEE Trans. Image Process. 10(10), 1579–1592 (2001)
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)
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)
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)
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)
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)
Wang, L., He, D.-C.: Texture classification using texture spectrum. Pattern Recogn. 23(8), 905–910 (1990)
He, D.-C., Wang, L.: Texture features based on texture spectrum. Pattern Recogn. 24(5), 391–399 (1991)
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)
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)
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)
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)
Xu, G., Shi, Y.Q.: Camera model identification using local binary patterns. In: IEEE International Conference on Multimedia and Expo (ICME) (2012)
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)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall, Upper Saddle River (2002)
Acknowledgement
Authors sincerely appreciate the kind help provided by Professors Yao Zhao and Rongrong Ni, Alex C. Kot and Dr. Gang Cao. Their codes have been used in our work to provide the performance comparison. This work has been partially supported by NSFC (61003297, U1135001, 61202415), the Knowledge Innovation Program of Shenzhen (JCYJ20130401170306848), the 863 Program (2011AA010503), NSF of Guangdong Province (S2013010011806), and the Shenzhen Peacock Program (KQCX20120816160011790, KQC201109050097A).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ding, F., Zhu, G., Shi, Y.Q. (2014). A Novel Method for Detecting Image Sharpening Based on Local Binary Pattern. In: Shi, Y., Kim, HJ., Pérez-González, F. (eds) Digital-Forensics and Watermarking. IWDW 2013. Lecture Notes in Computer Science(), vol 8389. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43886-2_13
Download citation
DOI: https://doi.org/10.1007/978-3-662-43886-2_13
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-43885-5
Online ISBN: 978-3-662-43886-2
eBook Packages: Computer ScienceComputer Science (R0)