Smartphone image acquisition forensics using sensor fingerprint
- Author(s): Ana Lucila Sandoval Orozco 1 ; Luis Javier García Villalba 1 ; David Manuel Arenas González 1 ; Jocelin Rosales Corripio 1 ; Julio Hernandez-Castro 2 ; Stuart James Gibson 3
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Volume 9, Issue 5,
October 2015,
p.
723 – 731
DOI: 10.1049/iet-cvi.2014.0243 , Print ISSN 1751-9632, Online ISSN 1751-9640
The forensic analysis of digital images from mobile devices is particularly important given their quick expansion and everyday use in the society. A further consequence of digital images' widespread use is that they are used today as silent witnesses in legal proceedings, as crucial evidence of the crime. This study specifically addresses the description of a technique that allows the identification of the image source acquisition, for the specific case of mobile devices images. This approach is to extract wavelet-based features from sensor pattern noise which are then classified using a support vector machine. Moreover, there are a number of parameters that allows the authors to adapt the execution of the algorithm to specific situations desired for the forensic analyst (a variety of types and sizes of image or optimising the average accuracy rate in terms of processing time). This article describes a set of experiments with the same set of images that can obtain general conclusions for the different configurations.
Inspec keywords: support vector machines; image forensics; smart phones; image classification; mobile computing; fingerprint identification; wavelet transforms; feature extraction; image sensors
Other keywords: image source acquisition identification; wavelet-based feature extraction; sensor pattern noise; digital image; support vector machine; smartphone image acquisition forensics; sensor fingerprint; mobile devices; image classification
Subjects: Integral transforms; Computer vision and image processing techniques; Optical, image and video signal processing; Knowledge engineering techniques; Data security; Mobile, ubiquitous and pervasive computing; Integral transforms
References
-
-
1)
-
17. Liu, Q., Li, X., Chen, L., et al: ‘Identification of smartphone-image source and manipulation’, in Jiang, H., Ding, W., Ali, M., Wu, X. (Eds.): ‘Advanced research in applied artificial intelligence’ (Springer, Berlin Heidelberg, Dalian, China, 2012), pp. 262–271.
-
-
2)
-
23. Chen, M., Fridrich, J., Goljan, M., Lukas, J.: ‘Determining image origin and integrity using sensor noise’, IEEE Trans. Inf. Forensics Sec., 2008, 3, (1), pp. 74–90 (doi: 10.1109/TIFS.2007.916285).
-
-
3)
-
5. Al-Zarouni, M.: ‘Mobile handset forensic evidence: a challenge for law enforcement’. Proc. Fourth Australian Digital Forensics Conference, School of Computer and Information Science, Edith Cowan University, December 2006.
-
-
4)
-
19. Lukas, J., Fridrich, J., Goljan, M.: ‘Digital camera identification from sensor pattern noise’, IEEE Trans. Inf. Forensics Sec., 2006, 1, (2), pp. 205–214 (doi: 10.1109/TIFS.2006.873602).
-
-
5)
-
16. Ozparlak, L., Avcibas, I.: ‘Differentiating between images using wavelet-based transforms: A comparative study’, IEEE Trans. Inf. Forensics Sec., 2011, 6, (4), pp. 1418–1431 (doi: 10.1109/TIFS.2011.2162830).
-
-
6)
-
1. ‘Gartner says smartphone sales grew 46.5 percent in second quarter of 2013 and exceeded feature phone sales for first time’, http://www.gartner.com/newsroom/id/2573415, 2013.
-
-
7)
-
22. Sandoval Orozco, A.L., Arenas Gonźalez, D.M., Corripio, J.R., Garćıa Villalba, L.J., Hernandez-Castro, J.C.: ‘Techniques for source camera identification’. Proc. Sixth Int. Conf. on Information Technology, May 2013, pp. 1–9.
-
-
8)
-
21. Li, C.T.: ‘Source camera linking using enhanced sensor pattern noise extracted from images’. Proc. Third Int. Conf. on Crime Detection and Prevention (ICDP 2009), Curran Associates, Inc., December 2009, pp. 1–6.
-
-
9)
-
13. Long, Y., Huang, Y.: ‘Image based source camera identification using demosaicking’. Proc. IEEE Eighth Workshop on Multimedia Signal Processing, October 2006, pp. 419–424.
-
-
10)
-
3. Embedded imaging takes off as stand-alone digital cameras stall, http://www.icinsights.com/news/bulletins/Embedded-Imaging-Takes-Off-As-Standalone-Digital-Cameras-Stall/, 2013.
-
-
11)
-
4. Baer, R.: ‘Resolution limits in digital photography: the looming end of the pixel wars – OSA technical digest (CD)’. Proc. Imaging Systems, Optical Society of America, June 2010, p. ITuB3.
-
-
12)
-
26. Adams, J., Parulski, K., Spaulding, K.: ‘Colorprocessing in digital cameras’, IEEE Micro,1998, 18, (6), pp. 20–30 (doi: 10.1109/40.743681).
-
-
13)
-
14. Celiktutan, O., Avcibas, I., Sankur, B., Ayerden, N.P., Capar, C.: ‘Source cell-phone identification’. Proc. IEEE 14th Signal Processing and Communications Applications, April 2006, pp. 1–3.
-
-
14)
-
9. Wang, B., Guo, Y., Kong, X., Meng, F.: ‘Source camera identification forensics based on wavelet features’. Proc. Int. Conf. on Intelligent Information Hiding and Multimedia Signal Processing, IEEE Computer Society, September 2009, pp. 702–705.
-
-
15)
-
28. Corripio, J.R., Arenas Gonźalez, D.M., Sandoval Orozco, A.L., Garćia Villalba, L.J., Hernandez-Castro, J.C., Gibson, S.J.: ‘Source smartphone identification using sensor pattern noise and wavelet transform’. Proc. Fifth Int. Conf. on Imaging for Crime Detection and Prevention (ICDP 2013), 16–17 December 2013, pp. 1–6.
-
-
16)
-
10. Van Lanh, T., Chong, K.S., Emmanuel, S., Kankanhalli, M.S.: ‘A survey on digital camera image forensic methods’. Proc. IEEE Int. Conf. on Multimedia and Expo, July 2007, pp. 16–19.
-
-
17)
-
2. Ahonen, T., Moore, A., Almanac, T.A.: ‘Mobile telecoms industry annual review’, 2012.
-
-
18)
-
7. Ramanath, R., Snyder, W.E., Yoo, Y., Drew, M.S.: ‘Colorimage processing pipeline’, IEEE Signal Process. Mag., 2005, 22, (1), pp. 34–43 (doi: 10.1109/MSP.2005.1407713).
-
-
19)
-
8. Mckay, C., Swaminathan, A., Gou, H., Wu, M.: ‘Image acquisition forensics: forensic analysis to identify imaging source’. Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, June 2008, pp. 1657–1660.
-
-
20)
-
6. Gloe, T., Kirchner, M., Winkler, A., Bohme, R.: ‘Can we trust digital image forensics?’. Proc. 15th Int. Conf. on Multimedia, September 2007, pp. 78–86.
-
-
21)
-
15. Meng, F.J., Kong, X.W., You, X.G.: ‘Source camera identification based on image bi-coherence and wavelet features’. Proc. Fourth Annual IFIP WG 11.9 Int. Conf. on Digital Forensics, Kyoto, Japan, January 2008.
-
-
22)
-
25. McKay, C.: ‘Forensic analysis of digital imaging devices’. Technical report, University of Maryland, 2007.
-
-
23)
-
11. Choi, K.S.: ‘Source camera identification using footprints from lens aberration’. Proc. Digital Photography II, number 852 in 6069, SPIE Int. Society For Optical Engineering, February 2006, pp. 60690J–60690J-8.
-
-
24)
-
12. Bayram, S., Sencar, H.T., Memon, N.: ‘Classification of digital camera-models based on demosaicing artifacts’, Digit. Invest., 2008, 5, (1–2), pp. 49–59 (doi: 10.1016/j.diin.2008.06.004).
-
-
25)
-
24. Celiktutan, O., Sankur, B., Avcibas, I.: ‘Blind identification of source cell-phone model’, IEEE Trans. Inf. Forensics Sec., 2008, 3, (3), pp. 553–566 (doi: 10.1109/TIFS.2008.926993).
-
-
26)
-
20. Costa, F.D.O., Eckmann, M., Scheirer, W.J., Rocha, A.: ‘Open set source camera attribution’. Proc. 25th Conf. on Graphics, Patterns and Images, IEEE, August 2012, pp. 71–78.
-
-
27)
-
27. Chang, C.C., Lin, C.J.: ‘LIBSVM: A library for support vector machines’, Version 3.17, 26 April 2013, http://www.csie.ntu.edu.tw/cjlin/libsvm/.
-
-
28)
-
18. Geradts, Z.J., Bijhold, J., Kieft, M., Kurosawa, K., Kuroki, K., Saitoh, N.: ‘Methods for identification of images acquired with digital cameras’. Proc. Enabling Technologies for Law Enforcement and Security, SPIE-Int. Society for Optical Engine, February 2001, vol. 4232, pp. 505–512.
-
-
1)
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