This is an open access article published by the IET under the Creative Commons Attribution-NonCommercial-NoDerivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/)
Picture sharing through social networks has become a prominent phenomenon, producing a large amount of data that law enforcers may be entitled to use, under the proper legal framework, as a source of information for investigating a crime. In this work, the authors exploit digital camera ‘fingerprinting’ based on noise residuals (sensor pattern noise or SPN) to achieve a novel forensic task, named picture-to-identity linking. It consists of finding social network accounts that possibly belong to the author of a certain photo (e.g. showing illegal content). The rationale is that the author of the offending photo has likely used the same camera for taking other (legal) pictures, and posted them in a social network account. The authors extend a previous work on the topic by coupling SPN with visual image similarity, a useful cue when pictures have been taken in the same environment (e.g. a room). The authors also improve the framework by allowing for multiple-image queries, and thoroughly evaluate the performance on two corpora of images from social network accounts, including the impact of image modifications. Reported results show a robust improvement with respect to the previous work, and prove the usefulness of picture-to-identity as an aid for digital forensic investigations.
References
-
-
1)
-
21. Goljan, M., Fridrich, J., Filler, T.: ‘Large scale test of sensor fingerprint camera identification’. Proc. SPIE, Electronic Imaging, Security and Forensics of Multimedia Contents XI, 2009, pp. 18–22.
-
2)
-
K.S. Choi ,
E.Y. Lam ,
K.K.Y. Wong
.
Automatic source camera identification using the intrinsic lens radial distortion.
Opt. Express
,
24 ,
11551 -
11565
-
3)
-
C.-T. Li
.
Source camera identification using enhanced sensor pattern noise.
IEEE Trans. Inf. Forensics Sec.
,
2 ,
280 -
287
-
4)
-
A.C. Popescu ,
H. Farid
.
Exposing digital forgeries in color filter array interpolated images.
IEEE Trans. Signal Process.
,
10 ,
3948 -
3959
-
5)
-
33. Jain, A.K., Flynn, P., Ross, A.A.: ‘Handbook of biometrics’ (Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2007).
-
6)
-
10. Amerini, I., Caldelli, R., Cappellini, V., Picchioni, F., Piva, A.: ‘Analysis of denoising filters for photo response non uniformity noise extraction in source camera identification’. 16th Int. Conf. on Digital Signal Processing, , July 2009, pp. 1–7.
-
7)
-
6. Geradts, Z.J.M.H.: ‘Content-based information retrieval from forensic image databases’ (Universiteit Utrecht, 2002).
-
8)
-
12. Long, Y., Huang, Y.: ‘Image based source camera identification using demosaicking’. IEEE Eighth Workshop on Multimedia Signal Processing, 2006, pp. 419–424.
-
9)
-
7. Wen, C.-Y., Yu, C.-C.: ‘Image retrieval of digital crime scene images’, Forensic Sci. J., 2005, 4, (1), pp. 37–45.
-
10)
-
B.S. Manjunath ,
J.R. Ohm ,
V.V. Vasudevan ,
A. Yamada
.
Color and texture descriptors.
IEEE Trans. Circuits Syst. Video Technol.
,
6 ,
703 -
715
-
11)
-
16. Van, L.T., Emmanuel, S., Kankanhalli, M.S.: ‘Identifying source cell phone using chromatic aberration’. IEEE Int. Conf. on Multimedia and Expo, 2007, pp. 883–886.
-
12)
-
A.E. Dirik ,
H.T. Sencar ,
N. Memon
.
Digital single lens reflex camera identification from traces of sensor dust.
IEEE Trans. Inf. Forensics Sec.
,
3 ,
539 -
552
-
13)
-
5. Chen, Y., Roussev, V., Richard, G., Gao, Y.: ‘Content-based image retrieval for digital forensics’, inPollitt, M., Shenoi, S. (Eds.): ‘Advances in digital forensics’ (Springer Boston, MA, 2005), pp. 271–282.
-
14)
-
J. Fridrich
.
Digital image forensic using sensor noise.
IEEE Signal Process. Mag.
,
2 ,
26 -
37
-
15)
-
C.-T. Li ,
Y. Li
.
Colour-decoupled photo response non-uniformity for digital image forensics.
IEEE Trans. Circuits Syst. Video Technol.
,
2 ,
260 -
271
-
16)
-
25. Zhou, X.S., Huang, T.S.: ‘Relevance feedback in image retrieval: A comprehensive review’, Multimedia Syst., 2003, 8, (6), pp. 536–544 (doi: 10.1007/s00530-002-0070-3).
-
17)
-
X. Kang ,
Y. Li ,
Z. Qu ,
J. Huang
.
Enhancing source camera identification performance with a camera reference phase sensor pattern noise.
IEEE Trans. Inf. Forensics Sec.
,
2 ,
393 -
402
-
18)
-
29. Palus, H.: ‘Representations of colour images in different colour spaces’, in Sangwine, S.J., Horne, R.E.N. (Eds.): ‘The colour image processing handbook’ (Springer US, 1998), pp. 67–90.
-
19)
-
14. Sorrell, M.J.: ‘Digital camera source identification through jpeg quantisation’, in Li, C.-T. (Ed.): ‘Multimedia forensics and security’ (Information Science Reference, 2009).
-
20)
-
J. Lukáš ,
J. Fridrich ,
M. Goljan
.
Digital camera identification from sensor pattern noise.
IEEE Trans. Inf. Forensics Sec.
,
2 ,
205 -
214
-
21)
-
J. Kittler
.
On combining classifiers.
IEEE Trans. Pattern Anal. Mach. Intell.
,
3 ,
226 -
239
-
22)
-
M. Chen ,
J. Fridrich ,
M. Goljan ,
J. Lukas
.
Determining image origin and integrity using sensor noise.
IEEE Trans. Inf. Forensics Secur.
,
1 ,
74 -
90
-
23)
-
26. Brown, R.A., Pham, B.L., De Vel, O.Y.: ‘A grammar for the specification of forensic image mining searches’. Eighth Australian and New Zealand Intelligent Information Systems Conf., Sydney, Australia, 2003, pp. 139–144.
-
24)
-
H. Cao ,
A.C. Kot
.
Accurate detection of demosaicing regularity for digital image forensics.
IEEE Trans. Inf. Forensics Sec.
,
4 ,
899 -
910
-
25)
-
24. Lew, M.S., Sebe, N., Djeraba, C., Jain, R.: ‘Content-based multimedia information retrieval: State of the art and challenges’, ACM Trans. Multimedia Comput. Commun. Appl., 2006, 2, (1), pp. 1–19 (doi: 10.1145/1126004.1126005).
-
26)
-
28. Heflin, B., Scheirer, W., Boult, T.E.: ‘Detecting and classifying scars, marks, and tattoos found in the wild’. Proc. of the 2012 IEEE Fifth Int. Conf. on Biometrics: Theory, Applications and Systems (BTAS), September 2012, pp. 31–38.
-
27)
-
22. Bayram, S., Sencar, H.T., Memon, N.: ‘Efficient sensor fingerprint matching through fingerprint binarization’, IEEE Trans. Inf. Forensics Sec., 2012, 7, (4), pp. 1404–1413 (doi: 10.1109/TIFS.2012.2192272).
-
28)
-
31. Chatzichristofis, S.A., Boutalis, Y.S.: ‘Fcth: Fuzzy color and texture histogram – a low level feature for accurate image retrieval’. Ninth Int. Workshop on Image Analysis for Multimedia Interactive Services WIAMIS ‘08, May 2008, pp. 191–196.
-
29)
-
19. Li, C.-T., Satta, R.: ‘On the location-dependent quality of the sensor pattern noise and its implication in multimedia forensics’. Proc. of the Fourth Int. Conf. on Imaging for Crime Detection and Prevention 2011 (ICDP 2011), 2011.
-
30)
-
Y. Liu ,
D. Zhang ,
G. Lu ,
W.-Y. Ma
.
A survey of content-based image retrieval with high-level semantics.
Pattern Recognit.
,
1 ,
262 -
282
-
31)
-
20. Li, C.-T., Satta, R.: ‘Empirical investigation into the correlation between vignetting effect and the quality of sensor pattern noise’, IET Comput. Vis., 2012, 6, pp. 560–566(6) (doi: 10.1049/iet-cvi.2012.0044).
-
32)
-
4. Satta, R., Stirparo, P.: ‘Picture-to-identity linking of social network accounts based on sensor pattern noise’. Proc. of the Fifth Int. Conf. on Imaging for Crime Detection and Prevention 2011 (ICDP 2013), 2013.
-
33)
-
23. Gisolf, F., Malgoezar, A., Baar, T., Geradts, Z.: ‘Improving source camera identification using a simplified total variation based noise removal algorithm’, Digital Invest., 2013, 10, (3), pp. 207–214 (doi: 10.1016/j.diin.2013.08.002).
-
34)
-
27. Brown, R.A., Pham, B.L., De Vel, O.Y.: ‘Design of a digital forensics image mining system’. Proc. of the Int. Workshop on Intelligent Information Hiding and Multimedia Signal Processing, 2005.
-
35)
-
34. Casey, E.: ‘Handbook of digital forensics and investigation’ (Academic Press, 2009).
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