Science China Information Sciences

, Volume 57, Issue 2, pp 1–18 | Cite as

A framework for identifying shifted double JPEG compression artifacts with application to non-intrusive digital image forensics

Research Paper


Non-intrusive digital image forensics (NIDIF) is a novel approach to authenticate the trustworthiness of digital images. It works by exploring varieties of intrinsic characteristics involved in the digital imaging, editing, storing processes as discriminative features to reveal the subtle traces left by a malicious fraudster. The NIDIF for the lossy JPEG image format is of special importance for its pervasive application. In this paper, we propose an NIDIF framework for the JPEG images. The framework involves two complementary identification methods for exposing shifted double JPEG (SD-JPEG) compression artifacts, including an improved ICA-based method and a First Digits Histogram based method. They are designed to treat the detectable conditions and a few special undetectable conditions separately. Detailed theoretical justifications are provided to reveal the relationship between the detectability of the artifacts and some intrinsic statistical characteristics of natural image signal. The extensive experimental results have shown the effectiveness of the proposed methods. Furthermore, some case studies are also given to demonstrate how to reveal certain types of image manipulations, such as cropping, splicing, or both, with our framework.


non-intrusive digital image forensics SD-JPEG re-compression artifacts image authentication 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Mehdi K L, Sencar H T, Memon N. Blind source camera identification. In: Proceedings of the IEEE International Conference on Image Processing, Singapore, 2004. 709–712Google Scholar
  2. 2.
    Fridrich J, Soukal D, Lukáš J. Detection of copy-move forgery in digital images. In: Proceedings of Digital Forensic Research Workshop, Cleveland, 2003Google Scholar
  3. 3.
    Farid H. Image forgery detection. Signal Process Mag, 2009, 26: 16–25CrossRefGoogle Scholar
  4. 4.
    Farid H. Detecting digital forgeries using bispectral analysis. Technical Report AIM-1657. 1999Google Scholar
  5. 5.
    Popescu A C, Farid H. Exposing digital forgeries by detecting traces of resampling. IEEE Trans Signal Process, 2005, 53: 758–767CrossRefGoogle Scholar
  6. 6.
    Shi Y Q, Chen C H, Chen W. A natural image model approach to splicing detection. In: Proceedings of ACM Workshop on Multimedia and Security, Dallas, 2007. 51–62Google Scholar
  7. 7.
    Kee E, Farid H. Digital image authentication from thumbnails. In: Proceedings of SPIE 7541, Media Forensics and Security II, San Francisco, 2010. 75410EGoogle Scholar
  8. 8.
    Kee E, Johnson M K, Farid H. Digital image authentication from jpeg headers. IEEE Trans Inf Forens Secur, 2011, 6: 1066–1075CrossRefGoogle Scholar
  9. 9.
    Bayram S, Sencar H, Memon N. Identifying digital cameras using cfa interpolation. In: Proceedings of IFIP International Conference on Digital Forensics. New York: Springer, 2006. 289–299Google Scholar
  10. 10.
    Johnson M K, Farid H. Exposing digital forgeries through chromatic aberration. In: Proceedings of ACM Workshop on Multimedia and Security, Geneva, 2006. 48–55Google Scholar
  11. 11.
    Chen M, Fridrich J, Lukáš J, et al. Imaging sensor noise as digital X-ray for revealing forgeries. In: Proceedings of the 9th Information Hiding Workshop, Saint Malo, 2007. 342–358CrossRefGoogle Scholar
  12. 12.
    Ng T T, Chang S F, Tsui M P. Using geometry invariants for camera response function estimation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, 2007. 1–8Google Scholar
  13. 13.
    Johnson M K, Farid H. Exposing digital forgeries in complex lighting environments. IEEE Trans Inf Forens Secur, 2007, 2: 450–461CrossRefGoogle Scholar
  14. 14.
    Kee E, Farid H. Exposing digital forgeries from 3-d lighting environments. In: Proceedings of IEEE International Workshop on Information Forensics and Security, Seattle, 2010. 1–6Google Scholar
  15. 15.
    O’Brien F J, Farid H. Exposing photo manipulation with inconsistent reflections. ACM Trans Graph, 2012, 31: 4Google Scholar
  16. 16.
    Johnson M K, Farid H. Detecting photographic composites of people. Digital Watermark, 2008, 19–33CrossRefGoogle Scholar
  17. 17.
    Lukáš J, Fridrich J. Estimation of primary quantization matrix in double compressed jpeg images. In: Proceedings of Digital Forensic Research Workshop, Cleveland, 2003Google Scholar
  18. 18.
    Popescu A C. Statistical tools for digital image forensics. Dissertation for Ph.D. Degree. Hanover: Dartmouth College, 2005Google Scholar
  19. 19.
    Fu D D, Shi Y Q, Su W. A generalized benford’s law for jpeg coefficients and its applications in image forensics. In: Proceedings of SPIE 6505, Steganography, and Watermarking of Multimedia Contents IX, San Francisco, 2007. 65051LGoogle Scholar
  20. 20.
    He J F, Lin Z C, Wang L F, et al. Detecting doctored jpeg images via dct coefficient analysis. In: Proceedings of European Conference on Computer Vision, Graz, 2006. 423–435Google Scholar
  21. 21.
    Luo W Q, Qu Z H, Huang J W, et al. A novel method for detecting cropped and recompressed image block. In: Proceedings of the IEEE International Conference on Acoustics Speech and Signal Processing, Hawaii, 2007. 217–220Google Scholar
  22. 22.
    Qu Z H, Luo W Q, Huang J W. A convolutive mixing model for shifted double jpeg compression with application to passive image authentication. In: IEEE International Conference on Acoustics Speech and Signal Processing, Las Vegas, 2008. 1661–1664Google Scholar
  23. 23.
    Qu Z H, Luo W Q, Huang J W. Identifying shifted double jpeg compression artifacts for non-intrusive digital image forensics. In: Processing of International Conference on Computational Visual Media, Beijing, 2012. 1–8CrossRefGoogle Scholar
  24. 24.
    Hyvärinen A, Oja E. Independent component analysis: algorithms and applications. Neural Netw, 2000, 13: 411–430CrossRefGoogle Scholar
  25. 25.
    Bell A J, Sejnowski T J. An information-maximization approach to blind separation and blind deconvolution. Neural Comput, 1995, 7: 1129–1159CrossRefGoogle Scholar
  26. 26.
    Chang C C, Lin C J. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Techn, 2011, 2: 27CrossRefGoogle Scholar
  27. 27.
    Li B, Shi Y Q, Huang J W. Detecting doubly compressed jpeg images by using mode based first digit features. In: Processings of IEEE Workshop on Multimedia Signal Processing, 2008. 730–735Google Scholar
  28. 28.
    Fan Z G, Queiroz de R L. Identification of bitmap compression history: JPEG detection and quantizer estimation. IEEE Trans Image Process, 2003, 12: 230–235CrossRefGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.School of SoftwareSun Yat-Sen UniversityGuangzhouChina
  2. 2.Guangdong Research Institute of China TelecomGuangzhouChina
  3. 3.School of Information Science and TechnologySun Yat-Sen UniversityGuangzhouChina

Personalised recommendations