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access icon openaccess Sensor pattern noise and image similarity for picture-to-identity linking

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.


    1. 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. 1822.
    2. 2)
    3. 3)
    4. 4)
    5. 5)
      • 33. Jain, A.K., Flynn, P., Ross, A.A.: ‘Handbook of biometrics’ (Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2007).
    6. 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. 17.
    7. 7)
      • 6. Geradts, Z.J.M.H.: ‘Content-based information retrieval from forensic image databases’ (Universiteit Utrecht, 2002).
    8. 8)
      • 12. Long, Y., Huang, Y.: ‘Image based source camera identification using demosaicking’. IEEE Eighth Workshop on Multimedia Signal Processing, 2006, pp. 419424.
    9. 9)
      • 7. Wen, C.-Y., Yu, C.-C.: ‘Image retrieval of digital crime scene images’, Forensic Sci. J., 2005, 4, (1), pp. 3745.
    10. 10)
    11. 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. 883886.
    12. 12)
    13. 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. 271282.
    14. 14)
    15. 15)
    16. 16)
    17. 17)
    18. 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. 6790.
    19. 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. 20)
    21. 21)
    22. 22)
    23. 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. 139144.
    24. 24)
    25. 25)
    26. 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. 3138.
    27. 27)
    28. 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. 191196.
    29. 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. 30)
    31. 31)
    32. 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. 33)
    34. 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. 35)
      • 34. Casey, E.: ‘Handbook of digital forensics and investigation’ (Academic Press, 2009).

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