Universal Wavelet Relative Distortion: A New Counter–Forensic Attack on Photo Response Non-Uniformity Based Source Camera Identification

  • Venkata Udaya SameerEmail author
  • Ruchira Naskar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11125)


Photo Response Non–Uniformity (PRNU) is one of the most effective fingerprints used to detect the source camera of an image. Image Anonymization on the other hand, is a task of fooling the source camera identification, in order to protect the user’s anonymity in sensitive situations involving whistleblowers, social activists etc. To protect the privacy of users especially over the web, image anonymization is of huge importance. Counter–Forensic attacks on source camera identification try to make an image anonymous by nullifying the detection techniques. For almost every counter–forensic source camera identification attack, anti–counter attacks are being designed and hence there is a need to either strengthen the previous counter–forensic attacks or design a new attack altogether. In this work, we propose a new counter–forensic attack to source camera identification, using the Universal Wavelet Relative Distortion function designed for steganography. The main principle behind Universal Wavelet Relative Distortion is to embed changes in an image in regions such as textures or noisy parts which are crucial to source camera identification. We show through our experiments, when a random bit–string is inserted recursively in an image, the correlation strength of the noise residual based source camera identification gets significantly weak and such methods fail to map the source camera of the image under question. In the proposed method, the visual quality of the modified image is not changed, which makes our method a strong solution to image anonymization.


Cybercrime Counter forensics Digital forensics Fingerprint PCE PSNR SSIM Steganography Source camera identification 


  1. 1.
    Kobsa, A., Schreck, J.: Privacy through pseudonymity in user-adaptive systems. ACM Trans. Internet Technol. (TOIT) 3(2), 149–183 (2003)CrossRefGoogle Scholar
  2. 2.
    Zhu, Y., Xiong, L., Verdery, C.: Anonymizing user profiles for personalized web search. In: Proceedings of the 19th International Conference on World Wide Web, pp. 1225–1226. ACM (2010)Google Scholar
  3. 3.
    DeLeeuw, W.C., Smith, N.M.: Techniques and architecture for anonymizing user data. US Patent 9,589,151, 7 March 2017Google Scholar
  4. 4.
    Lukas, J., Fridrich, J., Goljan, M.: Digital camera identification from sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 1(2), 205–214 (2006)CrossRefGoogle Scholar
  5. 5.
    Goljan, M., Fridrich, J., Filler, T.: Large scale test of sensor fingerprint camera identification. In: Media Forensics and Security, vol. 7254, p. 72540I. International Society for Optics and Photonics (2009)Google Scholar
  6. 6.
    Li, C.-T.: Source camera identification using enhanced sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 5(2), 280–287 (2010)CrossRefGoogle Scholar
  7. 7.
    Lawgaly, A., Khelifi, F.: Sensor pattern noise estimation based on improved locally adaptive dct filtering and weighted averaging for source camera identification and verification. IEEE Trans. Inf. Forensics Secur. 12(2), 392–404 (2017)CrossRefGoogle Scholar
  8. 8.
    Goljan, M., Fridrich, J., Chen, M.: Defending against fingerprint-copy attack in sensor-based camera identification. IEEE Trans. Inf. Forensics Secur. 6(1), 227–236 (2011)CrossRefGoogle Scholar
  9. 9.
    Dirik, A.E., Karaküçük, A.: Forensic use of photo response non-uniformity of imaging sensors and a counter method. Optics Express 22(1), 470–482 (2014)CrossRefGoogle Scholar
  10. 10.
    Karaküçük, A., Dirik, A.E.: Adaptive photo-response non-uniformity noise removal against image source attribution. Digital Invest. 12, 66–76 (2015)CrossRefGoogle Scholar
  11. 11.
    Quiring, E., Kirchner, M.: Fragile sensor fingerprint camera identification. In: 2015 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–6. IEEE (2015)Google Scholar
  12. 12.
    Dirik, A.E., Sencar, H.T., Memon, N.: Analysis of seam-carving-based anonymization of images against prnu noise pattern-based source attribution. IEEE Trans. Inf. Forensics Secur. 9(12), 2277–2290 (2014)CrossRefGoogle Scholar
  13. 13.
    Zeng, H.: Rebuilding the credibility of sensor-based camera source identification. Multimed. Tools Appl. 75(21), 13871–13882 (2016)CrossRefGoogle Scholar
  14. 14.
    Taspinar, S., Mohanty, M., Memon, N.: PRNU based source attribution with a collection of seam-carved images. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 156–160. IEEE (2016)Google Scholar
  15. 15.
    Taspinar, S., Mohanty, M., Memon, N.: PRNU-based camera attribution from multiple seam-carved images. IEEE Trans. Inf. Forensics Secur. 12(12), 3065–3080 (2017)CrossRefGoogle Scholar
  16. 16.
    Li, H., Luo, W., Rao, Q., Huang, J.: Anti-forensics of camera identification and the triangle test by improved fingerprint-copy attack. arXiv preprint arXiv:1707.07795 (2017)
  17. 17.
    Goljan, M., Fridrich, J.J.: Sensor fingerprint digests for fast camera identification from geometrically distorted images. In: Media Watermarking, Security, and Forensics, p. 86650B (2013)Google Scholar
  18. 18.
    Chen, M., Fridrich, J., Goljan, M., Lukás, J.: Determining image origin and integrity using sensor noise. IEEE Trans. Inf. Forensics Secur. 3(1), 74–90 (2008)CrossRefGoogle Scholar
  19. 19.
    Holub, V., Fridrich, J.: Digital image steganography using universal distortion. In: Proceedings of the First ACM Workshop on Information Hiding and Multimedia Security, pp. 59–68. ACM (2013)Google Scholar
  20. 20.
    Holub, V., Fridrich, J., Denemark, T.: Universal distortion function for steganography in an arbitrary domain. EURASIP J. Inf. Secur. 2014(1), 1 (2014)CrossRefGoogle Scholar
  21. 21.
    Gloe, T., Böhme, R.: The dresden image database for benchmarking digital image forensics. J. Digit. Forensic Pract. 3(2–4), 150–159 (2010)CrossRefGoogle Scholar
  22. 22.
    Caldelli, R., Amerini, I., Novi, A.: An analysis on attacker actions in fingerprint-copy attack in source camera identification. In: 2011 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–6. IEEE (2011)Google Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Institute of TechnologyRourkelaIndia

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