Multimedia Tools and Applications

, Volume 77, Issue 24, pp 31835–31854 | Cite as

Anti-forensics of JPEG compression detection schemes using approximation of DCT coefficients

  • Tanmoy Kanti DasEmail author


Here we propose a unique anti-forensics method using the approximation of DCT coefficients for security analysis of forensics schemes that are dependent on the distortions produced by JPEG compression to detect forgery. Approximation process first builds a model of the AC component of DCT coefficients. Then it uses that model to restore the values of the DCT coefficients. The beauty of the proposed anti-forensics technique is that one can specify any distortion measure and it is capable of minimizing that distortion as long as the specified distortion is produced by JPEG compression or the distortion is measurable in the DCT domain. We specifically mount our anti-forensics technique on three leading JPEG compression detection schemes (Fan and de Queiroz, IEEE Trans Image Process 12(2):230–235, 2003; Lai and Böhme 2011) to highlight their weaknesses. Though these schemes are based on three different distortions metrics, still we could achieve \(100\%\) missed detection rate for JPEG images having quality factor more than equal to \(60\%\). Our analysis raises a serious question regarding the robustness and security of JPEG artifact based forgery detection schemes.


JPEG compression detection Anti-forensics Approximation 


  1. 1.
    Bas P, Filler T, Pevny T (2011) Break our steganographic system: The ins and outs of organizing boss. In: Proc. 13th Int. Conf. on information hiding, lecture notes computer science, vol 6958, pp 59–70CrossRefGoogle Scholar
  2. 2.
    Bianchi T, Piva A (2012) Image forgery localization via block-grained analysis of jpeg artifacts. IEEE Trans Inf Forensics Secur 7(3):1003–1017CrossRefGoogle Scholar
  3. 3.
    Birajdar GK, Mankar VH (2013) Digital image forgery detection using passive techniques: A survey. Digit Investig 10(3):226–245CrossRefGoogle Scholar
  4. 4.
    Böhme R, Kirchner M (2012) Counter-forensics: Attacking image forensics. In: Digital Image Forensics, pp 327–366. SpringerGoogle Scholar
  5. 5.
    Chakravarti L (1967) Roy handbook of methods of applied statistics- volume I. Wiley, New YorkGoogle Scholar
  6. 6.
    Cui J, Liu Y, Xu Y, Zhao H, Zha H (2013) Tracking generic human motion via fusion of low- and high-dimensional approaches. IEEE Trans Syst Man Cybern Syst 43(4):996–1002CrossRefGoogle Scholar
  7. 7.
    Das TK, Maitra S (2004) Cryptanalysis of correlation-based watermarking schemes using single watermarked copy. IEEE Signal Process Lett 11(4):446–449CrossRefGoogle Scholar
  8. 8.
    Das TK, Maitra S, Mitra J (2005) Cryptanalysis of optimal differential energy watermarking (DEW) and a modified robust scheme. IEEE Trans Signal Process 53 (2-2):768–775MathSciNetCrossRefGoogle Scholar
  9. 9.
    Fan W, Wang K, Cayre F, Xiong Z (2013) A variational approach to jpeg anti-forensics. In: 2013 IEEE international conference on acoustics, speech and signal processing, pp 3058–3062Google Scholar
  10. 10.
    Fan W, Wang K, Cayre F, Xiong Z (2014) Jpeg anti-forensics with improved tradeoff between forensic undetectability and image quality. IEEE Trans Inf Forensics Secur 9(8):1211–1226CrossRefGoogle Scholar
  11. 11.
    Fan Z, de Queiroz RL (2003) Identification of bitmap compression history Jpeg detection and quantizer estimation. IEEE Trans Image Process 12(2):230–235CrossRefGoogle Scholar
  12. 12.
    Farid H (2009) A survey of image forgery detection. IEEE Signal Processing Mag 2(26):16–25CrossRefGoogle Scholar
  13. 13.
    Iuliani M, Fanfani M, Colombo C, Piva A (2017) Reliability assessment of principal point estimates for forensic applications. J Visual Commun Image Represent 42:65–77CrossRefGoogle Scholar
  14. 14.
    Khan MK, Zakariah M, Malik H, Choo K-KR (2017) A novel audio forensic data-set for digital multimedia forensics. Aust J Forensic Sci 0(0):1–18Google Scholar
  15. 15.
    Lai S, Böhme R (2011) Countering counter-forensics: The case of jpeg compression. In: Information Hiding: 13th International Conference, IH 2011, Prague, Czech Republic, May 18-20, 2011, Revised Selected Papers. Springer Berlin Heidelberg, Berlin, pp 285–298CrossRefGoogle Scholar
  16. 16.
    Lam EY, Goodman JW (2000) A mathematical analysis of the dct coefficient distributions for images. IEEE Trans Image Process 9(10):1661–1666CrossRefGoogle Scholar
  17. 17.
    Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: Sensor-based activity recognition. Neurocomputing 181:108–115. Big Data Driven Intelligent Transportation SystemsCrossRefGoogle Scholar
  18. 18.
    Liu Y, Zheng Y, Liang Y, Liu S, Rosenblum DS (2016) Urban water quality prediction based on multi-task multi-view learning. In: Proceedings of the 25th international joint conference on artificial intelligence, IJCAI’16, pp 2576–2582. AAAI PressGoogle Scholar
  19. 19.
    Luo W, Huang J, Qui G (2010) Jpeg error analysis and its applications to digital image forensics. IEEE Trans Inf Forensics Secur 5(3):480–491CrossRefGoogle Scholar
  20. 20.
    Rosa AD, Fontani M, Massai M, Piva A, Barni M (2015) Second-order statistics analysis to cope with contrast enhancement counter-forensics. IEEE Signal Process Lett 22(8):1132–1136CrossRefGoogle Scholar
  21. 21.
    Stamm MC, Tjoa SK, Lin WS, Liu KJR (2010) Anti-forensics of jpeg compression In: 2010 IEEE international conference on acoustics, speech and signal processing, pp 1694–1697Google Scholar
  22. 22.
    Sutthiwan P, Shi YQ (2012) Anti-forensics of double jpeg compression detection. In: Digital Forensics and Watermarking: 10th International Workshop, IWDW 2011, Atlantic City, NY, October 23-26, 2011, Revised Selected Papers, pp 411–424. Berlin, Springer Berlin HeidelbergCrossRefGoogle Scholar
  23. 23.
    Ullerich C, Westfeld A (2008) Weakness of mb2. In: IWDW 2007, pp 127–142Google Scholar
  24. 24.
    Valenzise G, Nobile V, Tagliasacchi M, Tubaro S (2011) Countering jpeg anti-forensics. In: 2011 18th IEEE international conference on image processing, pp 1949–1952Google Scholar
  25. 25.
    Ye L, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2activity: Recognizing complex activities from sensor data. In: Proceedings of the 24th international conference on artificial intelligence, IJCAI’15, pp 1617–1623. AAAI PressGoogle Scholar
  26. 26.
    Ye L, Zhang L, Nie L, Yan Y, Rosenblum S (2016) Fortune teller: Predicting your career path. In: Proceedings of the 13th AAAI conference on artificial intelligence, AAAI’16, pp 201–207. AAAI PressGoogle Scholar
  27. 27.
    Zakariah M, Khan MK, Malik H (2018) Digital multimedia audio forensics: past, present and future. Multimedia Tools Appl 77(1):1009–1040CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of Computer ApplicationsNational Institute of Technology RaipurRaipurIndia

Personalised recommendations