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Scaling factor estimation on JPEG compressed images by cyclostationarity analysis

  • Xianjin Liu
  • Wei Lu
  • Tao Huang
  • Hongmei Liu
  • Yingjie Xue
  • Yuileong Yeung
Article
  • 75 Downloads

Abstract

Scaling factor estimation is one of the most important topics in image forensics. The existing methods mainly employ the peak of the Fourier spectrum of the variance on image difference to detect the scaling factor. However, when the image is compressed, there will be additional stronger peaks which greatly affect the detection ability. In this paper, a novel method to estimate the scaling factor on JPEG compressed images in the presence of image scaling before the compression is proposed. We find the squared image difference can more effectively obtain the resampling characteristics, and we will mathematically show its periodicity. To further improve the detection ability, we analyze the flat block. It also produces periodic peaks in the spectrum, meanwhile which are enhanced by JPEG compression. To solve this problem, a method based on interpolation on the flat block is developed to remove these influences. The experimental results demonstrate that the proposed detection method outperforms some state-of-the-art methods.

Keywords

Image forensics Image resampling detection Scaling factor estimation 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. U1736118), the National Key R&D Program of China (No. 2017YFB0802500), the Natural Science Foundation of Guangdong (No. 2016A030313350), the Special Funds for Science and Technology Development of Guangdong (No. 2016KZ010103), the Key Project of Scientific Research Plan of Guangzhou (No. 201804020068), the Fundamental Research Funds for the Central Universities (No. 16lgjc83 and No. 17lgjc45).

References

  1. 1.
    Battiato S, Farinella GM, Messina E, Puglisi G (2012) Robust image alignment for tampering detection. IEEE Trans Inf Forensics Secur 7(4):1105–1117CrossRefGoogle Scholar
  2. 2.
    Bianchi T, Piva A (2012) Reverse engineering of double JPEG compression in the presence of image resizing. In: International workshop on information forensics and security, pp 127–132Google Scholar
  3. 3.
    Birajdar G, H.Mankar V (2014) Blind method for rescaling detection and rescale factor estimation in digital images using periodic properties of interpolation. AEUE - International Journal of Electronics and Communications 68Google Scholar
  4. 4.
    Chen L, Lu W, Ni J (2012) An image region description method based on step sector statistics and its application in image copy-rotate/flip-move forgery detection. Int J Digital Crime Forensics 4(1):49–62CrossRefGoogle Scholar
  5. 5.
    Chen L, Lu W, Ni J, Sun W, Huang J (2013) Region duplication detection based on harris corner points and step sector statistics. J Vis Commun Image Represent 24(3):244–254CrossRefGoogle Scholar
  6. 6.
    Chen C, Ni J, Shen Z (2014) Effective estimation of image rotation angle using spectral method. IEEE Signal Process Lett 21(7):890–894CrossRefGoogle Scholar
  7. 7.
    Chen C, Ni J, Shen Z, Shi YQ (2017) Blind forensics of successive geometric transformations in digital images using spectral method: Theory and applications. IEEE Trans Image Process 26(6):2811–2824MathSciNetCrossRefGoogle Scholar
  8. 8.
    Chen J, Lu W, Fang Y, Liu X, Yeung Y, Xue Y (2018) Binary image steganalysis based on local texture patternGoogle Scholar
  9. 9.
    Dalgaard N, Mosquera C, Pérez-González F (2010) On the role of differentiation for resampling detection. In: International conference on image processing, pp 1753–1756Google Scholar
  10. 10.
    Feng X, Cox IJ, Doerr G (2012) Normalized energy density-based forensic detection of resampled images. IEEE Trans Multimed 14(3):536–545CrossRefGoogle Scholar
  11. 11.
    Feng B, Wei Z, Sun W, Huang J, Shi Y (2015) Robust image watermarking based on tucker decomposition and adaptive-lattice quantization index modulation 41Google Scholar
  12. 12.
    Gallagher AC (2005) Detection of linear and cubic interpolation in jpeg compressed images. In: Canadian conference on computer and robot vision, pp 65–72Google Scholar
  13. 13.
    Gloe T, Bohme R (2010) The ‘Dresden Image Database’ for benchmarking digital image forensics. In: ACM symposium on applied computing, vol 2, pp 1585–1591Google Scholar
  14. 14.
    He Z, Lu W, Sun W, Huang J (2012) Digital image splicing detection based on markov features in DCT and DWT domain. Pattern Recogn 45(12):4292–4299CrossRefGoogle Scholar
  15. 15.
    Johnson MK, Farid H (2005) Exposing digital forgeries by detecting inconsistencies in lighting. In: ACM workshop on multimedia and security, pp 1–10. ACMGoogle Scholar
  16. 16.
    Kao YT, Lin HJ, Wang CW, Pai YC (2012) Effective detection for linear up-sampling by a factor of fraction. IEEE Trans Image Process 21(8):3443–3453MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Kirchner M (2008) Fast and reliable resampling detection by spectral analysis of fixed linear predictor residue. In: ACM workshop on multimedia and security, pp 11–20. New York, NY, USAGoogle Scholar
  18. 18.
    Kirchner M (2010) Linear row and column predictors for the analysis of resized images. In: ACM Workshop on Multimedia and Security. ACM, New York, pp 13–18Google Scholar
  19. 19.
    Kirchner M, Gloe T (2009) On resampling detection in re-compressed images. In: International workshop on information forensics and security, pp 21–25Google Scholar
  20. 20.
    Li L, Xue J, Tian Z (2013) Moment feature based forensic detection of resampled digital images. In: ACM International Conference on Multimedia. ACM, New York, pp 569–572Google Scholar
  21. 21.
    Li J, Yang F, Lu W, Sun W (2016) Keypoint-based copy-move detection scheme by adopting mscrs and improved feature matching. Multimedia Tools and Applications, pp 1–15Google Scholar
  22. 22.
    Lin ZX, Peng F, Long M (2017) A reversible watermarking for authenticating 2d vector graphics based on bionic spider web. Signal Process Image Commun 57:134–146CrossRefGoogle Scholar
  23. 23.
    Lukas J, Fridrich J, Goljan M (2006) Digital camera identification from sensor pattern noise. IEEE Trans Inf Forensics Secur 1(2):205–214CrossRefGoogle Scholar
  24. 24.
    Ma Y, Luo X, Li X, Bao Z, Zhang Y (2018) Selection of rich model steganalysis features based on decision rough set α-positive region reduction. IEEE Transactions on Circuits and Systems for Video Technology, pp 1–1Google Scholar
  25. 25.
    Mahdian B, Saic S (2008) Blind authentication using periodic properties of interpolation. IEEE Trans Inf Forensics Secur 3(3):529–538CrossRefGoogle Scholar
  26. 26.
    Nataraj L, Sarkar A, Manjunath BS (2009) Adding gaussian noise to ”denoise” JPEG for detecting image resizing. In: International conference on image processing, pp 1477–1480Google Scholar
  27. 27.
    Nguyen HC, Katzenbeisser S (2013) Detecting resized double JPEG compressed images - using support vector machine. In: International conference on communications and multimedia security, pp 113–122Google Scholar
  28. 28.
    Panchal UH, Srivastava R (2015) A comprehensive survey on digital image watermarking techniques. In: International Conference on Communication Systems and Network Technologies, pp 591–595. Gwalior, IndiaGoogle Scholar
  29. 29.
    Popescu A, Farid H (2005) Exposing digital forgeries in color filter array interpolated images. IEEE Trans Signal Process 53(10):3948–3959MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    Popescu AC, Farid H (2005) Exposing digital forgeries by detecting traces of resampling. IEEE Trans Signal Process 53(2):758–767MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    Qian R, Li W, Yu N, Hao Z (2012) Image forensics with rotation-tolerant resampling detection. In: International conference on multimedia and expo workshops, pp 61–66Google Scholar
  32. 32.
    Sathe VP, Vaidyanathan PP (1993) Effects of multirate systems on the statistical properties of random signals. IEEE Transactions on Signal Processing 41(1)Google Scholar
  33. 33.
    Stamm MC, Wu M, Liu KJR (2013) Information forensics an overview of the first decade. IEEE Access 1:167–200CrossRefGoogle Scholar
  34. 34.
    Vázquez-Padín D, Pérez-González F (2011) Prefilter design for forensic resampling estimation. In: International workshop on information forensics and security, vol 00, pp 1–6Google Scholar
  35. 35.
    Vázquez-Padín D, Comesaña P (2012) Ml estimation of the resampling factor. In: International workshop on information forensics and security, pp 205–210Google Scholar
  36. 36.
    Vázquez-Padín D, Mosquera C, Pérez-González F (2010) Two-dimensional statistical test for the presence of almost cyclostationarity on images. In: International conference on image processing, pp 1745–1748Google Scholar
  37. 37.
    Vázquez-Padín D, Comesaña P, Pérez-González F (2015) An svd approach to forensic image resampling detection. In: European signal processing conference, pp 2067–2071Google Scholar
  38. 38.
    Vázquez-Padín D, Pérez-González F, Comesaña-Alfaro P (2017) A random matrix approach to the forensic analysis of upscaled images. IEEE Trans Inf Forensics Secur 12(9):2115–2130CrossRefGoogle Scholar
  39. 39.
    Vyas C, Lunagaria M (2014) A review on methods for image authentication and visual cryptography in digital image watermarking. In: International conference on computational intelligence and computing research, pp 1-6. Coimbatore, IndiaGoogle Scholar
  40. 40.
    Wei W, Wang S, Zhang X, Tang Z (2010) Estimation of image rotation angle using interpolation-related spectral signatures with application to blind detection of image forgery. IEEE Trans Inf Forensics Secur 5(3):507–517CrossRefGoogle Scholar
  41. 41.
    Wolberg G (1994) Digital image warping, 1st edn. IEEE Computer Society Press, Los AlamitosGoogle Scholar
  42. 42.
    Xue F, Ye Z, Lu W, Liu H, Li B (2017) Mse period based estimation of first quantization step in double compressed JPEG images. Signal Process Image Commun 57:76–83CrossRefGoogle Scholar
  43. 43.
    Yang F, Li J, Lu W, Weng J (2017) Copy-move forgery detection based on hybrid features. Eng Appl Artif Intel 59:73–83CrossRefGoogle Scholar
  44. 44.
    Zhang Q, Lu W, Weng J (2016) Joint image splicing detection in dct and contourlet transform domain. J Vis Commun Image Represent 40:449–458CrossRefGoogle Scholar
  45. 45.
    Zhang F, Lu W, Liu H, Xue F (2018) Natural image deblurring based on l0-regularization and kernel shape optimization. Multimedia Tools and ApplicationsGoogle Scholar
  46. 46.
    Zhang Q, Lu W, Wang R, Li G (2018) Digital image splicing detection based on markov features in block dwt domain. Multimedia Tools and ApplicationsGoogle Scholar
  47. 47.
    Zhang Y, Qin C, Zhang W, Liu F, Luo X (2018) On the fault-tolerant performance for a class of robust image steganography. Signal ProcessingGoogle Scholar
  48. 48.
    Zhu N, Deng C, Gao X (2016) A learning-to-rank approach for image scaling factor estimation. Neurocomputing 204:33–40CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Data and Computer Science, Guangdong Key Laboratory of Information Security TechnologySun Yat-sen UniversityGuangzhouChina
  2. 2.State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of SciencesBeijingChina

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