Multimedia Tools and Applications

, Volume 74, Issue 15, pp 5557–5575 | Cite as

In-camera JPEG compression detection for doubly compressed images

Article

Abstract

An illicit photography work can be exposed by its unusual compression history. Our work aims at revealing the primary JPEG compression of a camera image especially when it has undergone an out-camera JPEG compression. The proposed method runs a recompression operator on a given image using a chosen software tool (MATLAB). We measure the JPEG error between the given image and the recompressed version in the Y, Cb and Cr color channels. The in-camera compression can be easily identified by drawing the JPEG error curves. In this paper a simple and high effective method is presented for automatically detecting the compression history of an image. For a doubly compressed image, the proposed method can give the historical compression sequence with the corresponding quality factors and determine whether the first compression is the in-camera compression. Experimental results, carried out on two datasets, show that the proposed method can yield satisfactory detection accuracy, over 96 % accuracy rate for in-camera compression and no false positives with a block size of 512 × 512. The proposed method has universality. It can be applied to multi-compression detection and is robust to different sources of out-camera compression, e.g. Adobe Photoshop. This makes it more practical compared to the previous methods of double compression.

Keywords

Digital image forensics In-camera compression Double JPEG compression detection Quantization tables 

References

  1. 1.
    Bas FTP, Pevny T (2011) Break our steganographic system - the ins and outs of organizing BOSS. In: Proceeding Information Hiding Conference. pp 59–70Google Scholar
  2. 2.
  3. 3.
    Chen SYC, Su W (2008) A machine learning based scheme for double jpeg compression detection. In: International conference on pattern recognition. pp 1–4Google Scholar
  4. 4.
    Cox MMIJ, Bloom J (1999) Digital watermarking, 2002. Morgan KaufmannGoogle Scholar
  5. 5.
    Fan Z, de Queiroz R (2003) Identification of bitmap compression history: JPEG detection and quantizer estimation. IEEE Trans Image Process 12(2):230–235CrossRefGoogle Scholar
  6. 6.
    Farid H (2009) Exposing digital forgeries from JPEG ghosts. IEEE Trans Inf Forensic Secur 4(1):154–160MathSciNetCrossRefGoogle Scholar
  7. 7.
    Fridrich GMJ, Hogea D (2003) Steganalysis of jpeg images: breaking the f5 algorithm. In: Information hiding. Springer, pp 310–323Google Scholar
  8. 8.
    Fu SYD, Su W (2007) A generalized benford’s law for jpeg coefficients and its applications in image forensics. SPIE electronic imaging: security, steganography, and watermarking of multimedia contentsGoogle Scholar
  9. 9.
    Hamdy EMHRMS, Kahlifa E (2010) Quantization table estimation in JPEG images. Int J Adv Comput Sci Appl 1(6):17–23Google Scholar
  10. 10.
    Hamilton E (1992) JPEG file interchange format. http://www.jpeg.org/public/jfif.pdf
  11. 11.
    He LZWLJ, Tang X (2006) Detecting doctored JPEG images via dct coefficient analysis. In: European conference on computer vision. pp 423–435Google Scholar
  12. 12.
    Huang HJF, Shi Y (2010) Detecting double jpeg compression with the same quantization matrix. IEEE Trans Inf Forensic Secur 5(4):848–856CrossRefGoogle Scholar
  13. 13.
    Jain A (1989) Fundamentals of digital image processing. Prentice-Hall, IncGoogle Scholar
  14. 14.
    Katzenbeisser S, Petitolas F (2000) Information hiding techniques for steganography and digital watermaking. Taylor & FrancisGoogle Scholar
  15. 15.
    Kee JME, Farid H (2011) Digital image authentication from JPEG headers. IEEE Trans Inf Forensic Secur 99:1–1Google Scholar
  16. 16.
    Kormblum J (2008) Using jpeg quantization tables to identify imagery processed by software. In: Proceedings digital forensic workshop. ELSEVIER, pp 21–25Google Scholar
  17. 17.
    Li SYB, Huang J (2008) Detecting doubly compressed jpeg images by using mode based first digit features. In: IEEE workshop on multimedia signal processing. pp 730–735Google Scholar
  18. 18.
    Lin CMG, Chen Y (2011) A passive-blind forgery detection scheme based on content-adaptive quantization table estimation. IEEE Trans Circ Syst Vid Technol 99:1–1Google Scholar
  19. 19.
    Lin HJTXZ, Tang C (2009) Fast, automatic and fine-grained tampered jpeg image detection via dct coefficient analysis. Pattern Recog 42(11):2492–2501MATHMathSciNetCrossRefGoogle Scholar
  20. 20.
    Lukáš J, Fridrich J (2003) Estimation of primary quantization matrix in double compressed JPEG images. In: Proceedings digital forensic research workshop. pp 5–8Google Scholar
  21. 21.
    Luo HJW, Qiu G (2010) Jpeg error analysis and its applications to digital image forensics. IEEE Trans Inf Forensic Secur 5(3):480–491CrossRefGoogle Scholar
  22. 22.
    Milani TMS, Tubaro S (2012) Discriminating multiple jpeg compression using first digit features. In: IEEE international conference on acoustics, speech, and signal processingGoogle Scholar
  23. 23.
    Ng TT, Chang SF (2004) A model for image splicing. In: IEEE International Conference on Image Processing (ICIP)Google Scholar
  24. 24.
    Pevnỳ T, Fridrich J (2008) Estimation of primary quantization matrix for steganalysis of double-compressed jpeg images. In: Proceedings SPIE, electronic imaging, security, forensics, steganography, and watermarking of multimedia contents X 6819:11Google Scholar
  25. 25.
    Popescu A, Farid H (2005) Statistical tools for digital forensics. In: Information hiding. Springer, pp 395–407Google Scholar
  26. 26.
    Robertson M, Stevenson R (2005) Dct quantization noise in compressed images. IEEE Trans Circ Syst Vid Technol 15(1):27–38CrossRefGoogle Scholar
  27. 27.
    Sutthiwan SYZHNTP, Su W (2011) Markovian rake transform for digital image tampering detection. In: Transactions on data hiding and multimedia security VI. pp 1–17Google Scholar
  28. 28.
    Ye SQS, Chang E (2007) Detecting digital image forgeries by measuring inconsistencies of blocking artifact. In: IEEE international conference on multimedia and expo. pp 12–15Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Ningbo UniversityNingboChina

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