Multimedia Tools and Applications

, Volume 77, Issue 5, pp 5241–5261 | Cite as

A fuzzy fusion approach for modified contrast enhancement based image forensics against attacks

  • B. Subrahmanyeswara Rao


In today’s digital age the trustworthy towards image is distorting because of malicious forgery images. The issues related to the multimedia security have led to the research focus towards tampering detection. The main objective of the work is to develop robust and forensic detection framework against post processing. It is also essential to enhance the security against attacks. In this paper, a Modified Contrast Enhancement based Forensics (MCEF) method based on Fuzzy Fusion is proposed against post-processing activity. First, we check for the histogram peaks and gaps as a result of contrast enhancement which is used in the latest technique. From the standpoint of attackers, we use two types of attacks, CE trace hiding attack and CE trace forging attack, which could invalidate the forensic detector and fabricate two types of forensic errors, consequently. The CE trace hiding attack is implemented by integrating local random dithering into the form of pixel value mapping. The CE trace forging attack is proposed by modifying the grey level histogram of a target pixel region to fraudulent peak/gap artifacts. Then both attacks are added to enhanced images as a post processing activity. As a result the gaps get disappeared, but introduced sudden peaks. Then, feature selection methods in conjunction with fuzzy fusion approach is suggested to enhance the robustness of tamper detection methods. The threshold value for contrast detection is increased, so we can identify the contrast enhancement. The Artificial Neural Network (ANN) is used instead of SVM, it increases the robustness and accuracy of the digital images. The proposed methodology will be implemented using MATLAB and validated by comparing with the conventional techniques.


Contrast enhancement CE trace hiding attack CE trace forging attack Fuzzy fusion Artificial neural network 


  1. 1.
    Arici T, Dikbas S, Altunbasak Y (2009) A histogram modification framework and its application for image contrast enhancement. IEEE Trans Image Process 18(9):1921–1935MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Bayram S, Avcubas I, Sankur B, Memon N (2006) Image manipulation detection. J Electron Imag 15(4):04110201–04110217CrossRefGoogle Scholar
  3. 3.
    Bianchi T, Piva A (2012) Detection of non-aligned double JPEG compression based on integer periodicity maps. IEEE Trans Inf Forensics Secur 7(2):842–848CrossRefGoogle Scholar
  4. 4.
    Cao H, Kot AC (2012) Manipulation detection on image patches using fusion boost. IEEE Trans Inf Forensics Secur 7(3):992–1002CrossRefGoogle Scholar
  5. 5.
    Cao G, Zhao Y, Ni R (2010) Forensic estimation of gamma correction in digital images. Proc. 17th IEEE Int. Conf. Image Process 2097–2100Google Scholar
  6. 6.
    Cao G, Zhao Y, Ni R, Li X (2014) Contrast enhancement-based forensics in digital images. IEEE Trans Inf Forensics Secur 9(3):515–525CrossRefGoogle Scholar
  7. 7.
    Charpe J, Bhattacharya A (2015) Revealing image forgery through image manipulation detection. IEEE, In Communication Technologies (GCCT) 723–727Google Scholar
  8. 8.
    Chen C, Ni J, Huang J (2013) Blind detection of median filtering in digital images: a difference domain based approach. IEEE Trans Image Process 22(12):4699–4710MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    De Rosa A, 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
  10. 10.
    Fan J, Cao H, Kot AC (2013) Estimating EXIF parameters based on noise features for image manipulation detection. IEEE Trans Inf Forensics Secur 8(4):608–618CrossRefGoogle Scholar
  11. 11.
    Farid H (2009) Image forgery detection. IEEE signal process. Mag 26(2):16–25Google Scholar
  12. 12.
    Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874MathSciNetCrossRefGoogle Scholar
  13. 13.
    Huang S-C, Cheng F-C, Chiu Y-S (2013) Efficient contrast enhancement using adaptive gamma correction with weighting distribution. IEEE Trans Image Process 22(3):1032–1041MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Mahdian B, Saic S (2010) A bibliography on blind methods for identifying image forgery. Image Commun 25(6):389–399Google Scholar
  15. 15.
    O’Brien J, Farid H (2012) Exposing photo manipulation with inconsistent reflections. ACM Trans Graph 31(1):1–11CrossRefGoogle Scholar
  16. 16.
    Pevny T, Fridrich J (2008) Detection of double-compression in jpeg images for applications in steganography. IEEE Trans on Inf Forensics and Secur 3(2):247–258CrossRefGoogle Scholar
  17. 17.
    Popescu A.C, Farid H (2004) Statistical tools for digital forensics. In 6th Intl. Workshop on Info. Hiding, Toronto, CanadaGoogle Scholar
  18. 18.
    Popescu AC, Farid H (2005) Exposing digital forgeries by detecting traces of resampling. IEEE Trans Signal Process 53(2):758–767MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Qureshi MA, Deriche M (2015) A bibliography of pixel-based blind image forgery detection techniques. Signal Process Image Commun 39:46–74CrossRefGoogle Scholar
  20. 20.
    Ravi H, Subramanyam AV, Emmanuel S (2016) ACE–an effective anti-forensic contrast enhancement technique. IEEE Signal Process Lett 23(2):212–216CrossRefGoogle Scholar
  21. 21.
    Sornalatha ST, Mahalakshmi SD, Vijayalakshmi K (2015) Detecting contrast enhancement based image forgeries by parallel approach. IEEE, In Electronics and Communication Systems (ICECS) 1162–1167Google Scholar
  22. 22.
    Stamm MC, Liu KJR (2010a) Forensic detection of image manipulation using statistical intrinsic fingerprints. IEEE Trans. Inf. Forensics Secur 5(3):492–506CrossRefGoogle Scholar
  23. 23.
    Stamm MC, Liu KR (2010b) Forensic detection of image manipulation using statistical intrinsic fingerprints. IEEE Trans. Inf. Forensics Secur 5(3):492–506CrossRefGoogle Scholar
  24. 24.
    Swaminathan A, Wu M, Liu KJR (2008) Digital image forensics via intrinsic fingerprints. IEEE Trans Inf Forensics Secur 3(1):101–117CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringMLR Institute of TechnologyHyderabadIndia

Personalised recommendations