JPEG image tampering localization based on normalized gray level co-occurrence matrix

  • Fei Xue
  • Wei Lu
  • Ziyi Ye
  • Hongmei Liu


To locate the tampered region of double compressed JPEG images, one of the most effective methods is based on the statistical characteristic of the images. After tampering operation, the tampered region and the original region will have different statistical distributions. And according to this cue, the histogram of DCT coefficients can be moded as the mixture of the distributions of DCT coefficients in tampered and untampered regions. By estimating each distribution of the mixture model, the probability of being tampered of each DCT block can be calculated and the final localization result can be obtained. Thus the mixture model will significant impact the result. In this paper, a novel mixture model based on normalized gray level co-occurrence matrix (NGLCM) is proposed for tampering localization in JPEG images. Firstly, NGLCM is used to measure the conditional probabilities of being tampered regions and being untampered regions for each 8 × 8 DCT coefficient block, which can take advantage of both the statistical characteristic of double quantization effect and the relationship among neighboring blocks. Then the Bayesian posterior probability map is generated by the conditional probabilities, which indicates the tampering probability of each block. Finally, the map is refined based on a inter-block connectivity and Gaussian weighted filter strategy to determine the final tampered region location. Experimental results demonstrate that the proposed approach can localize the tampered region of JPEG images with a satisfactory performance and outperforms the state-of-the-art methods.


Image forensics Double compressed JPEG image Tampered region localization Normalized gray level co-occurrence matrix 



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).


  1. 1.
    Amerini I, Becarelli R, Caldelli R, Mastio AD (2014) Splicing forgeries localization through the use of first digit features. In: IEEE international workshop on information forensics and security (WIFS). Atlanta, pp 143–148Google Scholar
  2. 2.
    Battiato S, Farinella GM, Messina E, Puglisi G (2012) Robust image alignment for tampering detection. IEEE Tran Inf Forens Secur 7(4):1105–1117CrossRefGoogle Scholar
  3. 3.
    Bayar B, Stamm MC (2016) A deep learning approach to universal image manipulation detection using a new convolutional layer. In: ACM workshop on information hiding and multimedia security. New York, pp 5–10Google Scholar
  4. 4.
    Bianchi T, Piva A (2012) Image forgery localization via block-grained analysis of JPEG artifacts. IEEE Trans Inf Forens Secur 7(3):1003–1017CrossRefGoogle Scholar
  5. 5.
    Bianchi T, Rosa AD, Piva A (2011) Improved dct coefficient analysis for forgery localization in jpeg images. In: IEEE international conference on acoustics, speech and signal processing, pp 2444–2447Google Scholar
  6. 6.
    Casia tampered image detection evaluation database. [Online]
  7. 7.
    Chen J, Kang X, Liu Y, Wang ZJ (2015) Median filtering forensics based on convolutional neural networks. IEEE Signal Process Lett 22(11):1849–1853CrossRefGoogle 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.
    Dong L, Kong X, Wang B, You X (2011) A robust jpeg image tampering detection method using GLCM features. Adv Inf Sci Serv Sci 3(10):384–391Google Scholar
  10. 10.
    Farid H (2009) Exposing digital forgeries from JPEG ghosts. IEEE Trans Inf Forens Secur 4(1):154–160CrossRefGoogle Scholar
  11. 11.
    Farid H (2009) A survey of image forgery detection. IEEE Signal Process Mag 26(2):16–25CrossRefGoogle Scholar
  12. 12.
    Fergus R, Singh B, Hertzmann A, Roweis ST, Freeman WT (2006) Removing camera shake from a single photograph. Acm Trans Graph 2006 25(3):787–794CrossRefzbMATHGoogle Scholar
  13. 13.
    Fu D, Shi YQ, Su W (2007) A generalized Benfords law for JPEG coefficients and its applications in image forensics. In: SPIE electronic imaging: security, steganography, and watermarking of multimedia contents, vol 6505. San JoseGoogle Scholar
  14. 14.
    Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern smc-3(6):610–621CrossRefGoogle Scholar
  15. 15.
    Healey G, Kondepudy R (1994) Radiometric CCD camera calibration and noise estimation. IEEE Trans Pattern Anal Mach Intell 16(3):267–276CrossRefGoogle Scholar
  16. 16.
    Johnson MK, Farid H (2005) Exposing digital forgeries by detecting incon sistencies in lighting. In: Proceedings of the 7th workshop on multimedia and security. New York, pp 1–10Google Scholar
  17. 17.
    Korus P, Huang J (2016) Multi-scale fusion for improved localization of malicious tampering in digital images. IEEE Trans Image Process 25(3):1312–1326MathSciNetCrossRefGoogle Scholar
  18. 18.
    Lee JC, Chang CP, Chen WK (2015) Detection of copycmove image forgery using histogram of orientated gradients. Inform Sci 321:250–262CrossRefGoogle Scholar
  19. 19.
    Li B, Shi YQ, Huang J (2008) Detecting doubly compressed JPEG images by using mode based first digit features. In: IEEE 10th workshop on multimedia signal processing. Cairns, pp 730–735Google Scholar
  20. 20.
    Lin Z, He J, Tang X, Tang CK (2009) Fast, automatic and fine-grained tampered JPEG image detection via dct coefficient analysis. Pattern Recogn 42 (11):2492–2501CrossRefzbMATHGoogle Scholar
  21. 21.
    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 Trans Circ Syst Vid Technol, 1–1Google Scholar
  22. 22.
    Mahdian B, Saic S (2008) Blind authentication using periodic properties of interpolation. IEEE Trans Inf Forens Secur 3(3):529–538CrossRefGoogle Scholar
  23. 23.
    Makbol NM, Khoo BE, Rassem TH, Loukhaoukha K (2017) A new reliable optimized image watermarking scheme based on the integer wavelet transform and singular value decomposition for copyright protection. Inform Sci 417:381–400CrossRefGoogle Scholar
  24. 24.
    Popescu AC, Farid H (2004) Statistical tools for digital forensics. In: International conference on information hiding, pp 128–147Google Scholar
  25. 25.
    Popescu AC, Farid H (2005) Exposing digital forgeries in color filter array interpolated images. IEEE Trans Signal Process 53(10):3948C3959MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Schaefer G, Stich M (2004) UCID: an uncompressed color image database. In: Storage and retrieval methods and applications for multimedia. San Jose, pp 472–480Google Scholar
  27. 27.
    Stamm MC, Liu KR (2010) Forensic detection of image manipulation using statistical intrinsic fingerprints. IEEE Trans Inf Forens Secur 5(3):492–506CrossRefGoogle Scholar
  28. 28.
    Suhail MA, Obaidat MS, Ipson SS, Sadoun B (2003) A comparative study of digital watermarking in JPEG and JPEG 2000 environments. Inform Sci 151 (3):93–105CrossRefzbMATHGoogle Scholar
  29. 29.
    Usage of image file formats for websites. [Online]
  30. 30.
    Wang W, Dong J, Tan T (2010) Tampered region localization of digital color images based on jpeg compression noise. In: 9th international workshop on digital watermarking. Seoul, p 120C133Google Scholar
  31. 31.
    Wang XY, Niu PP, Yang HY, Wang CP, Wang AL (2014) A new robust color image watermarking using local quaternion exponent moments. Inform Sci 277(2):731–754CrossRefGoogle Scholar
  32. 32.
    Wang W, Dong J, Tan T (2014) Exploring DCT coefficient quantization effects for local tampering detection. IEEE Trans Inf Forens Secur 9(10):1653–1666CrossRefGoogle Scholar
  33. 33.
    Wang C, Yang H, Meinel C (2015) Deep semantic mapping for cross-modal retrieval. In: IEEE international conference on tools with artificial intelligence. Washington, DC, pp 234–241Google Scholar
  34. 34.
    Wang C, Yang H, Meinel C (2016) A deep semantic framework for multimodal representation learning. Multimedia Tools Appl 75(15):9255–9276CrossRefGoogle Scholar
  35. 35.
    Wang C, Yang H, Bartz C, Meinel C (2016) Image captioning with deep bidirectional lstms. In: Proceedings of the 2016 ACM on multimedia conference. New York, pp 988–997Google Scholar
  36. 36.
    Wang XY, Liu YN, Xu H, Wang AL, Yang HY (2016) Blind optimum detector for robust image watermarking in nonsubsampled shearlet domain. Inform Sci 372:634–654CrossRefGoogle Scholar
  37. 37.
    Wang C, Yang H, Meinel C (2018) Image captioning with deep bidirectional lstms and multi-task learning. ACM Trans Multimed Comput Commun Appl 14(2):1–20Google Scholar
  38. 38.
    Ye S, Sun Q, Chang EC (2007) Detecting digital image forgeries by measuring inconsistencies of blocking artifact. In: Proceedings of the IEEE international conference on multimedia and expo. Beijing, pp 12–15Google Scholar
  39. 39.
    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
  40. 40.
    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
  41. 41.
    Zhao F, Yu Z, Li S (2010) Detecting double compressed JPEG images by using moment features of mode based DCT histograms. In: International conference on multimedia technology (ICMT). Ningbo, pp 1–4Google 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

Personalised recommendations