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Scaling factor estimation on JPEG compressed images by cyclostationarity analysis

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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.

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References

  1. Battiato S, Farinella GM, Messina E, Puglisi G (2012) Robust image alignment for tampering detection. IEEE Trans Inf Forensics Secur 7(4):1105–1117

    Article  Google Scholar 

  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 127–132

  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 68

  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):49–62

    Article  Google Scholar 

  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):244–254

    Article  Google Scholar 

  6. Chen C, Ni J, Shen Z (2014) Effective estimation of image rotation angle using spectral method. IEEE Signal Process Lett 21(7):890–894

    Article  Google Scholar 

  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):2811–2824

    Article  MathSciNet  Google Scholar 

  8. Chen J, Lu W, Fang Y, Liu X, Yeung Y, Xue Y (2018) Binary image steganalysis based on local texture pattern

  9. Dalgaard N, Mosquera C, Pérez-González F (2010) On the role of differentiation for resampling detection. In: International conference on image processing, pp 1753–1756

  10. Feng X, Cox IJ, Doerr G (2012) Normalized energy density-based forensic detection of resampled images. IEEE Trans Multimed 14(3):536–545

    Article  Google Scholar 

  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 41

  12. Gallagher AC (2005) Detection of linear and cubic interpolation in jpeg compressed images. In: Canadian conference on computer and robot vision, pp 65–72

  13. Gloe T, Bohme R (2010) The ‘Dresden Image Database’ for benchmarking digital image forensics. In: ACM symposium on applied computing, vol 2, pp 1585–1591

  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):4292–4299

    Article  Google Scholar 

  15. Johnson MK, Farid H (2005) Exposing digital forgeries by detecting inconsistencies in lighting. In: ACM workshop on multimedia and security, pp 1–10. ACM

  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):3443–3453

    Article  MathSciNet  Google Scholar 

  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, USA

  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 13–18

  19. Kirchner M, Gloe T (2009) On resampling detection in re-compressed images. In: International workshop on information forensics and security, pp 21–25

  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 569–572

  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 1–15

  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 57:134–146

    Article  Google Scholar 

  23. Lukas J, Fridrich J, Goljan M (2006) Digital camera identification from sensor pattern noise. IEEE Trans Inf Forensics Secur 1(2):205–214

    Article  Google Scholar 

  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 1–1

  25. Mahdian B, Saic S (2008) Blind authentication using periodic properties of interpolation. IEEE Trans Inf Forensics Secur 3(3):529–538

    Article  Google Scholar 

  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 1477–1480

  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 113–122

    Chapter  Google Scholar 

  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, India

  29. Popescu A, Farid H (2005) Exposing digital forgeries in color filter array interpolated images. IEEE Trans Signal Process 53(10):3948–3959

    Article  MathSciNet  Google Scholar 

  30. Popescu AC, Farid H (2005) Exposing digital forgeries by detecting traces of resampling. IEEE Trans Signal Process 53(2):758–767

    Article  MathSciNet  Google Scholar 

  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 61–66

  32. Sathe VP, Vaidyanathan PP (1993) Effects of multirate systems on the statistical properties of random signals. IEEE Transactions on Signal Processing 41(1)

    Article  Google Scholar 

  33. Stamm MC, Wu M, Liu KJR (2013) Information forensics an overview of the first decade. IEEE Access 1:167–200

    Article  Google Scholar 

  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 1–6

  35. Vázquez-Padín D, Comesaña P (2012) Ml estimation of the resampling factor. In: International workshop on information forensics and security, pp 205–210

  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 1745–1748

  37. Vázquez-Padín D, Comesaña P, Pérez-González F (2015) An svd approach to forensic image resampling detection. In: European signal processing conference, pp 2067–2071

  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):2115–2130

    Article  Google Scholar 

  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, India

  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):507–517

    Article  Google Scholar 

  41. Wolberg G (1994) Digital image warping, 1st edn. IEEE Computer Society Press, Los Alamitos

    Google Scholar 

  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 57:76–83

    Article  Google Scholar 

  43. Yang F, Li J, Lu W, Weng J (2017) Copy-move forgery detection based on hybrid features. Eng Appl Artif Intel 59:73–83

    Article  Google Scholar 

  44. Zhang Q, Lu W, Weng J (2016) Joint image splicing detection in dct and contourlet transform domain. J Vis Commun Image Represent 40:449–458

    Article  Google Scholar 

  45. Zhang F, Lu W, Liu H, Xue F (2018) Natural image deblurring based on l0-regularization and kernel shape optimization. Multimedia Tools and Applications

  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 Applications

  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 Processing

  48. Zhu N, Deng C, Gao X (2016) A learning-to-rank approach for image scaling factor estimation. Neurocomputing 204:33–40

    Article  Google Scholar 

Download references

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).

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Authors and Affiliations

  1. School of Data and Computer Science, Guangdong Key Laboratory of Information Security Technology, Sun Yat-sen University, Guangzhou, 510006, China

    Xianjin Liu, Wei Lu, Tao Huang, Hongmei Liu, Yingjie Xue & Yuileong Yeung

  2. State Key Laboratory of Information Security Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Wei Lu

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  1. Xianjin Liu
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  2. Wei Lu
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  3. Tao Huang
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  4. Hongmei Liu
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Correspondence to Wei Lu.

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Liu, X., Lu, W., Huang, T. et al. Scaling factor estimation on JPEG compressed images by cyclostationarity analysis. Multimed Tools Appl 78, 7947–7964 (2019). https://doi.org/10.1007/s11042-018-6411-9

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  • DOI: https://doi.org/10.1007/s11042-018-6411-9

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