Multimedia Tools and Applications

, Volume 77, Issue 18, pp 23411–23427 | Cite as

A forensic algorithm against median filtering based on coefficients of image blocks in frequency domain

  • Dong-ping Wang
  • Tiegang Gao
  • Fusheng Yang


Median filtering is a popular nonlinear denoising operator, it not only can be used for image enhancement, and it also is an effective tool in application of anti-forensics. So, the blind detection of median filtering is a particularly hot topic. Different from the existing median filtering forensic methods using the image pixel statistical features, this paper proposed a novel approach for detecting median filtering in digital images using coefficients of image blocks in frequency domain, based on the theory analysis and experiments test. Large numbers of experimental results show that the proposed approach achieved a high accuracy in median filtering detection and a good robustness of defending JPEG compression, the algorithm also can be used to locate the median filtering area. The approach achieves much better performance than the existing state-of-the-art methods with different format and size of image blocks, particularly when the image blocks are tiny and have high JPEG compression ratio.


Digital image forensics Median filtering Discrete cosine transform DCT subband coefficients 


  1. 1.
    Bayram S, Avcıbaş İ, Sankur B, Memon N (2006) Image manipulation detection. J Electron Imaging 15(4):041102–041102CrossRefGoogle Scholar
  2. 2.
    Cao G, Zhao Y, Ni R, Yu L, Tian H (2010) Forensic detection of median filtering in digital images. IEEE International Conference on Multimedia and Expo 26:89–94Google Scholar
  3. 3.
    Cao G, Zhao Y, Ni R, Kot AC (2011) Unsharp masking sharpening detection via overshoot artifacts analysis. IEEE Signal Process Lett 18(10):603–606CrossRefGoogle Scholar
  4. 4.
    Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27Google Scholar
  5. 5.
    Chen C, Ni J, Huang R, Huang J (2012) Blind median filtering detection using statistics in difference domain. Lect Notes Comput Sci 7692:1–15Google Scholar
  6. 6.
    Image corpus of the 1st IEEE IFS-TC image forensics challenge [DB/OL].
  7. 7.
    Ker AD, Böhme R (2008, February) Revisiting weighted stego-image steganalysis. In: Electronic Imaging 2008. International Society for Optics and Photonics, pp 681905–681905Google Scholar
  8. 8.
    Kirchner M, Bohme R (2008) Hiding traces of resampling in digital images. IEEE Trans Inf Forensics Secur 3(4):582–592CrossRefGoogle Scholar
  9. 9.
    Kirchner M, Fridrich J (2010) On detection of median filtering in digital images. Media Forensics and Security II 7541:754110–754112CrossRefGoogle Scholar
  10. 10.
    Kong X, Wang B, Yang M, Feng Y (2016) Multiple heterogeneous JPEG image hierarchical forensic. Advanced Multimedia and Ubiquitous Engineering. In: Lecture Notes in Electrical Engineering, Vol 393, pp. 509–516. Springer, SingaporeGoogle Scholar
  11. 11.
    Lin WS, Tjoa SK, Zhao HV, Liu KR (2009) Digital image source coder forensics via intrinsic fingerprints. IEEE Trans Inf Forensics Secur 4(3):460–475CrossRefGoogle Scholar
  12. 12.
    Liu A, Zhao Z, Zhang C, Su Y (2017) Median filtering forensics in digital images based on frequency-domain features. Multimedia Tools Appl 6:1–14Google Scholar
  13. 13.
    Luo W, Huang J, Qiu G (2010) Jpeg error analysis and its applications to digital image forensics. IEEE Trans Inf Forensics Secur 5(3):480–491CrossRefGoogle Scholar
  14. 14.
    Ng TT, Chang SF, Sun Q (2004) A data set of authentic and spliced image blocks. Columbia University, ADVENT Technical Report, p 203–2004Google Scholar
  15. 15.
    Pevny T, Fridrich J (2008) Detection of double-compression in jpeg images for applications in steganography. IEEE Trans Inf Forensics Secur 3(2):247–258CrossRefGoogle Scholar
  16. 16.
    Popescu AC, Farid H (2005) Exposing digital forgeries by detecting traces of resampling. IEEE Trans Signal Process 53(2):758–767MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Schaefer G, Stich M (2004) UCID: An uncompressed color image database. In: Storage & Retrieval Methods & Applications for Multimedia, pp 472–480Google Scholar
  18. 18.
    Singh G, Singh K (2018) Forensics for partially double compressed doctored JPEG images. Multimed Tools Appl 77:485–502CrossRefGoogle Scholar
  19. 19.
    Stamm MC, Liu KJR (2010) Forensic detection of image manipulation using statistical intrinsic fingerprints. IEEE Trans Inf Forensics Secur 5(3):492–506CrossRefGoogle Scholar
  20. 20.
    Stamm MC, Liu KR (2011) Anti-forensics of digital image compression. IEEE Trans Inf Forensics Secur 6(3):1050–1065CrossRefGoogle Scholar
  21. 21.
    Swaminathan A, Wu M, Liu KJR (2008) Digital image forensics via intrinsic fingerprints. IEEE Trans Inf Forensics Secur 3(1):101–117CrossRefGoogle Scholar
  22. 22.
    Taimori A, Razzazi F, Behrad A, Ahmadi A, Babaie-Zadeh M (2017) A novel forensic image analysis tool for discovering double JPEG compression clues. Multimedia Tools Appl 76:7749–7783CrossRefGoogle Scholar
  23. 23.
    Yang J, Ren H, Zhu G, Huang J, Shi YQ (2017) Detecting median filtering via two-dimensional AR models of multiple filtered residuals. Multimedia Tools Appl 4:1–23Google Scholar
  24. 24.
    Yuan HD (2011) Blind forensics of median filtering in digital images. IEEE Trans Inf Forensics Secur 6(4):1335–1345CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.College of SoftwareNankai UniversityTianjinChina
  2. 2.School of Computer and Information EngineeringTianjin Chengjian UniversityTianjinChina

Personalised recommendations