Using PNU-Based Techniques to Detect Alien Frames in Videos

  • Giuseppe Cattaneo
  • Gianluca Roscigno
  • Andrea Bruno
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10016)


In this paper we discuss about video integrity problem and specifically we analyze whether the method proposed by Fridrich et al. [16] can be exploited for forensic purposes. In particular Fridrich et al. proposed a solution to identify the source camera given an input image. The method relies on the Pixel Non-Uniformity (PNU) noise produced by the sensor and existing in any digital image.

We first present a wider scenario related to video integrity. Then we focus on a particular case of video forgery where sequences of frames, recorded by a different camera (in short, alien frames), could be added to the original video.

By means of experimental evaluation in specific real world forensic scenarios we analyzed the accuracy degree that this method can achieve and we evaluated the critical conditions where the results are not enough reliable to be considered in courts.

The results show that the method is robust, and alien frames can be reliably detected provided that the source device (or its faithful fingerprint) is available. Nevertheless the discussed method applies to a rather limited concept of video integrity (alien frames detection) and more extensive solutions, able to cover a wider range of application scenarios, would be required as well.


Digital image forensics Pixel non-uniformity noise Source camera identification Video forgery detection Video integrity 


  1. 1.
    Bayram, S., Sencar, H.T., Memon, N.: Video copy detection based on source device characteristics: a complementary approach to content-based methods. In: Proceedings of the 1st ACM International Conference on Multimedia Information Retrieval, pp. 435–442. ACM (2008)Google Scholar
  2. 2.
    Bayram, S., Sencar, H.T., Memon, N.: Efficient techniques for sensor fingerprint matching in large image and video databases. In: IS&T/SPIE Electronic Imaging, vol. 7541, pp. 1–8. International Society for Optics and Photonics (2010)Google Scholar
  3. 3.
    Castiglione, A., Cattaneo, G., Cembalo, M., Ferraro Petrillo, U.: Experimentations with source camera identification and online social networks. J. Ambient Intell. Humaniz. Comput. 4(2), 265–274 (2013)CrossRefGoogle Scholar
  4. 4.
    Cattaneo, G., Faruolo, P., Ferraro Petrillo, U.: Experiments on improving sensor pattern noise extraction for source camera identification. In: Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), pp. 609–616 (2012)Google Scholar
  5. 5.
    Cattaneo, G., Ferraro Petrillo, U., Roscigno, G., De Fusco, C.: A PNU-based technique to detect forged regions in digital images. ACIVS 2015. LNCS, vol. 9386, pp. 486–498. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-25903-1_42 CrossRefGoogle Scholar
  6. 6.
    Cattaneo, G., Roscigno, G.: A possible pitfall in the experimental analysis of tampering detection algorithms. In: 17th International Conference on Network-Based Information Systems (NBiS 2014), pp. 279–286 (2014)Google Scholar
  7. 7.
    Cattaneo, G., Roscigno, G., Ferraro Petrillo, U.: Experimental evaluation of an algorithm for the detection of tampered JPEG images. In: Linawati, Mahendra, M.S., Neuhold, E.J., Tjoa, A.M., You, I. (eds.) ICT-EurAsia 2014. LNCS, vol. 8407, pp. 643–652. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  8. 8.
    Cattaneo, G., Roscigno, G., Ferraro Petrillo, U.: A scalable approach to source camera identification over Hadoop. In: IEEE 28th International Conference on Advanced Information Networking and Applications (AINA), pp. 366–373. IEEE (2014)Google Scholar
  9. 9.
    Cheddad, A., Condell, J., Curran, K., McKevitt, P.: Digital image steganography: survey and analysis of current methods. Signal Process. 90(3), 727–752 (2010)CrossRefzbMATHGoogle Scholar
  10. 10.
    Chen, M., Fridrich, J., Lukáš, J., Goljan, M.: Imaging sensor noise as digital X-ray for revealing forgeries. In: Furon, T., Cayre, F., Doërr, G., Bas, P. (eds.) IH 2007. LNCS, vol. 4567, pp. 342–358. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Chen, M., Fridrich, J., Goljan, M., Lukáš, J.: Source digital camcorder identification using sensor photo response non-uniformity. In: Electronic Imaging 2007, p. 65051G. International Society for Optics and Photonics (2007)Google Scholar
  12. 12.
    Chen, M., Fridrich, J., Goljan, M., Lukáš, J.: Determining image origin and integrity using sensor noise. IEEE Trans. Inf. Forensics Secur. 3(1), 74–90 (2008)CrossRefGoogle Scholar
  13. 13.
    Chierchia, G., Cozzolino, D., Poggi, G., Sansone, C., Verdoliva, L.: Guided filtering for PRNU-based localization of small-size image forgeries. In: International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2014, pp. 6231–6235. IEEE (2014)Google Scholar
  14. 14.
    Farid, H.: Exposing digital forgeries from JPEG ghosts. IEEE Trans. Inf. Forensics Secur. 4(1), 154–160 (2009)CrossRefGoogle Scholar
  15. 15.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Fridrich, J., Lukáš, J., Goljan, M.: Digital camera identification from sensor noise. IEEE Trans. Inf. Secur. Forensics 1(2), 205–214 (2006)CrossRefGoogle Scholar
  17. 17.
    Fridrich, J., Goljan, M., Du, R.: Steganalysis based on JPEG compatibility. In: International Symposium on the Convergence of IT and Communications (ITCom), vol. 4518, pp. 275–280. International Society for Optics and Photonics (2001)Google Scholar
  18. 18.
    Goljan, M., Fridrich, J., Filler, T.: Large scale test of sensor fingerprint camera identification. In: IS&T/SPIE, Electronic Imaging, Security and Forensics of Multimedia Contents XI, vol. 7254, pp. 1–12. International Society for Optics and Photonics (2009)Google Scholar
  19. 19.
    Hsu, C.C., Hung, T.Y., Lin, C.W., Hsu, C.T.: Video forgery detection using correlation of noise residue. In: IEEE 10th Workshop on Multimedia Signal Processing, 2008, pp. 170–174. IEEE (2008)Google Scholar
  20. 20.
    Hyun, D.K., Lee, M.J., Ryu, S.J., Lee, H.Y., Lee, H.K.: Forgery detection for surveillance video. In: Hyun, D.-K., Lee, M.-J., Ryu, S.-J., Lee, H.-Y., Lee, H.-K. (eds.) The Era of Interactive Media, pp. 25–36. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  21. 21.
    ITU Telecommunication Standardization Sector: H.264: advanced video coding for generic audiovisual services, February 2016. Accessed 30 Apr 2016
  22. 22.
    Lukáš, J., Fridrich, J., Goljan, M.: Detecting digital image forgeries using sensor pattern noise. In: Electronic Imaging 2006, p. 60720Y. International Society for Optics and Photonics (2006)Google Scholar
  23. 23.
    Richardson, I.E.: H.264 and MPEG-4 Video Compression: Video Coding for Next-Generation Multimedia. Wiley, Hoboken (2004)Google Scholar
  24. 24.
    Ye, S., Sun, Q., Chang, E.C.: Detecting digital image forgeries by measuring inconsistencies of blocking artifact. In: IEEE International Conference on Multimedia and Expo 2007, pp. 12–15. IEEE, July 2007Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Giuseppe Cattaneo
    • 1
  • Gianluca Roscigno
    • 1
  • Andrea Bruno
    • 1
  1. 1.Dipartimento di InformaticaUniversità degli Studi di SalernoFiscianoItaly

Personalised recommendations