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Experimental Analysis of the Pixel Non Uniformity (PNU) in SEM for Digital Forensics Purposes

  • Andrea BrunoEmail author
  • Giuseppe Cattaneo
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1080)

Abstract

Recent years saw an explosion in the number of the counterfeit or stolen images in scientific papers. In particular in the field of biomedical science publication this is becoming a serious problem for the health and economic issues caused by this fraud [1].

In this paper we investigate the possibility to extend a technique commonly used in image forensics to associate a given image with the camera used to take it. The original technique, proposed by Fridrich et al. in [3] uses the PNU, a unique fingerprint present in each photo and generated by natural the imperfection in the silicium slice that composes the Charge-Coupled Device (CCD) sensor.

We analyze the quality of the PNU present in the residual noise by evaluating the quality of this noise using its variance. The experimental results shows that some PNU is still present in the residual noise, but is less than the one present in photo from digital cameras.

This technique of evaluation is promisingly because is possible to use also to speedup the source camera identification process in videos by excluding the frames that not preserving enough PNU in the residual noise.

Keywords

Biomedical images Plagiarism PNU Noise Sensor noise 

References

  1. 1.
    Bik, E.M., Casadevall, A., Fang, F.C.: The prevalence of inappropriate image duplication in biomedical research publications. MBio 7(3), e00809–16 (2016)CrossRefGoogle Scholar
  2. 2.
    Bruno, A., Cattaneo, G., Ferraro Petrillo, U., Narducci, F., Roscigno, G.: Distributed anti-plagiarism checker for biomedical images based on sensor noise. In: Battiato, S., Farinella, G.M., Leo, M., Gallo, G. (eds.) ICIAP 2017. LNCS, vol. 10590, pp. 343–352. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-70742-6_32CrossRefGoogle Scholar
  3. 3.
    Fridrich, J., Lukáš, J., Goljan, M.: Digital camera identification from sensor noise. IEEE Trans. Inf. Secur. Forensics 1(2), 205–214 (2006)CrossRefGoogle Scholar
  4. 4.
    Stern, A.M., Casadevall, A., Steen, R.G., Fang, F.C.: Financial costs and personal consequences of research misconduct resulting in retracted publications. Elife 3, e02956 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Università degli Studi di SalernoFiscianoItaly

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