Sensor Defects in Digital Image Forensic

Chapter

Abstract

Just as human fingerprints or skin blemishes can be used for forensic purposes, imperfections of digital imaging sensors can serve as unique identifiers in numerous forensic applications, such as matching an image to a specific camera, revealing malicious image manipulation and processing, and determining an approximate age of a digital photograph. There exist several different types of defects that are of interest to the forensic analysts caused by imperfections in manufacturing, physical processes occurring inside the camera, and by environmental factors. This chapter begins with analyzing the pixel defects, while pointing out their forensic potential. Then, specific problems are formulated as tasks involving detection or matching of defects and noise patterns. Practical algorithms for these tasks are developed within the framework of parameter estimation and signal detection theory. The performance of the algorithms is demonstrated in real world examples.

References

  1. 1.
    Amerini I, Caldelli R, Cappellini V, Picchioni F, Piva A (2009) Analysis of denoising filters for photo-response non-uniformity noise extraction in source camera identification. In: Proceedings of the 16th international conference on Digital Signal Processing, pp 511–517Google Scholar
  2. 2.
    Bayram S, Sencar HT, Memon N (2010) Efficient techniques for sensor fingerprint matching in large image and video databases. In: Memon N, Dittmann J (eds) Proceeedings of SPIE electronic media forensics and security XII, vol 7541, pp 09-01–09-12Google Scholar
  3. 3.
    Bloy GJ (March 2008) Blind camera fingerprinting and image clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(3):532–534CrossRefGoogle Scholar
  4. 4.
    Böhme R, Kirchner M (2009) Synthesis of color filter array pattern in digital images. In: Memon N, Dittmann J (eds) Proceedings of SPIE media forensics and security XI, vol 7254. San Jose, CA, pp 0K–0LGoogle Scholar
  5. 5.
    Caldelli R, Amerini I, Novi A (2011) An analysis on attacker actions in fingerprint-copy attack in source camera identification. In: Proceedings of IEEE WIFS, Iguazu Falls, BrazilGoogle Scholar
  6. 6.
    Çeliktutan O, Avcibas I, Sankur B (2007) Blind identification of cellular phone cameras. In: Delp EJ, Wong PW (eds) Proceedings of SPIE electronic imaging, steganography, security, and watermarking of multimedia contents IX, vol 6505, pp H1–H12Google Scholar
  7. 7.
    Celiktutan OB, Sankur B, Avcibas I (2008) Blind identification of source cell-phone model. IEEE Transactions on Information Forensics and Security 3(3):553–566CrossRefGoogle Scholar
  8. 8.
    Chen M, Fridrich J, Goljan M (2007) Digital imaging sensor identification (further study). In: Delp EJ, Wong PW (eds) Proceedings of SPIE electronic imaging, steganography, security, and watermarking of multimedia contents IX, vol 6505, pp 0P–0QGoogle Scholar
  9. 9.
    Chen M, Fridrich J, Goljan M (2007) Imaging sensor noise as digital x-ray for revealing forgeries. In: Furon T et al (eds.) Proceedings of ninth information hiding workshop, vol 4567 of LNCS, Springer-Verlag, Saint Malo, France, pp 342–358Google Scholar
  10. 10.
    Chen M, Fridrich J, Goljan M (2007) Source digital camcorder identification using ccd photo response nonuniformity. In: Delp EJ Wong PW (eds) Proceedings of SPIE electronic imaging, steganography, security, and watermarking of multimedia contents IX, vol 6505, pp 1G–1HGoogle Scholar
  11. 11.
    Chen M, Fridrich J, Goljan M (March 2008) Determining image origin and integrity using sensor noise. IEEE Transactions on Information Forensics and Security 1(1):74–90CrossRefGoogle Scholar
  12. 12.
    Chen M, Fridrich J, Goljan M (2010) Defending against fingerprint-copy attack in sensor-based camera identification. IEEE Trans Inform Security Forensics, submittedGoogle Scholar
  13. 13.
    Dudas J, Wu LM, Jung C, Chapman GH, Koren Z, Koren I (2007) In: Martin RA, DiCarlo JM, Sampat N (eds) Identification of in-field defect development in digital image sensors, vol 6502. SPIE, pp 65020YGoogle Scholar
  14. 14.
    Filler T, Fridrich J, Goljan M (2008) Using sensor pattern noise for camera model identification. In: Proceedings of IEEE international conference on image processing (ICIP)Google Scholar
  15. 15.
    El Gamal A, Fowler B, Min H, Liu X (1998) Modeling and estimation of FPN components in CMOS image sensors. In: Proceedings of SPIE solid state sensor arrays: development and applications II, vol 3301-20, pp 168–177Google Scholar
  16. 16.
    Gloe T, Franz E, Winkler A (2007) Forensics for flatbed scanners. In Delp EJ, Wong PW (eds) Proceedings of SPIE electronic imaging, steganography, security, and watermarking of multimedia contents IX, vol 6505, pps 1I–1JGoogle Scholar
  17. 17.
    Gloe T, Kirchner M, Winkler A, Boehme R (2007) Can we trust digital image forensics? In: Proceedings of the fifteenthth international conference on multimedia, Multimedia ’07, ACM, pp 78–86Google Scholar
  18. 18.
    Goljan M, Chen M, Fridrich J (2007) Identifying common source digital camera from image pairs. In: Proceedings of IEEE international conference on image processing (ICIP)Google Scholar
  19. 19.
    Goljan M, Filler T, Fridrich J (2009) Camera identification-large scale test. In: Memon N, Dittmann J (eds) Proceeedings of SPIE electronic media forensics and security XI, vol 7254, pp 0I-01–0I-12Google Scholar
  20. 20.
    Goljan M, Fridrich J (2008) Camera identification from printed images. In: Delp EJ, Wong PW, Memon N, Dittmann J (eds) Proceeedings of SPIE electronic imaging, security, forensics, steganography, and watermarking of multimedia contents X, vol 6819, pp OI1-OI12Google Scholar
  21. 21.
    Goljan M, Fridrich J (2008) Camera identification from scaled and cropped images. In: Delp E, Wong PW, Memon N, Dittmann J (eds) Proceeedings of SPIE electronic imaging, security, forensics, steganography, and watermarking of multimedia contents X, vol 6819, pp OE1-OE13Google Scholar
  22. 22.
    Goljan M, Fridrich J (2012) Sensor-fingerprint based identification of images corrected for lens distortion. In: Memon ND, Alattar A, Delp EJ (eds) Proceedings SPIE electronic imaging, media watermarking, security and forensics January 22–26, 2012Google Scholar
  23. 23.
    Goljan M, Fridrich J, Chen M (2010) Sensor noise camera identification: Countering counter-forensics. In: Memon N, Dittmann J, Alattar A (eds) Proceedings of SPIE electronic imaging, steganography, security, and watermarking of multimedia contents XII, vol 7541, pp 0S-01–0S-12, January 17–21 2010Google Scholar
  24. 24.
    Goljan M, Fridrich J, Filler T (2010) Managing a large database of camera fingerprints. In Memon N, Dittmann J, Alattar A (eds) Proceeedings of SPIE electronic media forensics and security XII, vol 7541, pp 08-01–08-12, January 2010Google Scholar
  25. 25.
    Gou H, Swaminathan A, Wu M (2007) Robust scanner identification based on noise features. In Delp EJ, Wong PW (eds) Proceedings of SPIE electronic imaging, steganography, security, and watermarking of multimedia contents IX, vol 6505, pp 0S–0TGoogle Scholar
  26. 26.
    Healey G, Kondepudy R. Radiometric CCD camera calibration and noise estimationGoogle Scholar
  27. 27.
    Healey GE, Kondepudy R (March 1994) Radiometric CCD camera calibration and noise estimation. IEEE Trans Pattern Anal Mach Intell 16(3):267–276CrossRefGoogle Scholar
  28. 28.
    Holst GC (1998) CCD Arrays, Cameras, and Displays, 2nd edn. JCD Publishing& SPIE Press, USAGoogle Scholar
  29. 29.
    Holt CR (1987) Two-channel detectors for arbitrary linear channel distortion. IEEE Trans Acoust Speech Signal Process ASSP-35(3):267–273Google Scholar
  30. 30.
    Janesick JR (2001) Scientific charge-coupled devices, vol PM83. SPIE Press MonographGoogle Scholar
  31. 31.
    Kay SM (1998) Fundamentals of statistical signal processing, vol I: Estimation Theory, vol II. Prentice Hall, Upper Saddle River, NJGoogle Scholar
  32. 32.
    Kay SM (1998) Fundamentals of statistical signal processing, Detection Theory, vol II. Prentice Hall, Englewood CliffsGoogle Scholar
  33. 33.
    Khanna N, Mikkilineni AK, Chiu GTC, Allebach JP, Delp EJ (2007) Forensic classification of imaging sensor types. In: Delp EJ, Wong PW (eds) Proceedings of SPIE electronic imaging, steganography, security, and watermarking of multimedia contents IX, vol 6505, pp U1–U9Google Scholar
  34. 34.
    Khanna N, Mikkilineni AK, Chiu GTC, Allebach JP, Delp EJ Scanner identification using sensor pattern noise. In: Delp EJ, Wong PW (eds) Proceedings of SPIE electronic imaging, security, steganography, and watermarking of multimedia contents IX, vol 6505Google Scholar
  35. 35.
    Kurosawa K, Kuroki K, Saitoh N (1999) CCD fingerprint method - identification of a video camera from videotaped images. In: Proceedings of IEEE international conference on image processing (ICIP), pp 537–540Google Scholar
  36. 36.
    Leung J, Chapman GH, Koren I, Koren Z (2008) Automatic detection of in-field defect growth in image sensors. In: DFT ’08: Proceedings of the 2008 IEEE international symposium on defect and fault tolerance of VLSI systems, IEEE Computer Society, Washington, DC, pp 305–313Google Scholar
  37. 37.
    Leung J, Chapman GH, Koren I, Koren Z (2009) Characterization of gain enhanced in-field defects in digital imagers. In: IEEE international symposium on defect and fault-tolerance in VLSI systems, pp 155–163Google Scholar
  38. 38.
    Leung J, Dudas J, Chapman GH, Koren I, Koren Z (2007) Quantitative analysis of in-field defects in image sensor arrays. In: 22nd IEEE international symposium on defect and fault-tolerance in VLSI systems, 2007. DFT ’07, pp 526–534Google Scholar
  39. 39.
    Li C-T (2009) Source camera identification using enhanced sensor pattern noise. In: Proceedings of IEEE ICIP, pp 7–11Google Scholar
  40. 40.
    Lukáš J, Fridrich J, Goljan M (2005) Determining digital image origin using sensor imperfections. In: Delp EJ, Wong PW (eds) Proceedings of SPIE, electronic imaging, security, steganography, and watermarking of multimedia contents VII, San Jose, CA, pp 249–260Google Scholar
  41. 41.
    Lukáš J, Fridrich J, Goljan M (June 2006) Digital camera identification from sensor pattern noise. IEEE Transactions on Information Forensics and Security 1(2):205–214CrossRefGoogle Scholar
  42. 42.
    Mao J, Bulan O, Sharma G, Datta S (2009) Device temporal forensics: an information theoretic approach. In: Proceedings of IEEE international conference on image processing (ICIP), vol 1Google Scholar
  43. 43.
    Mihcak MK, Kozintsev I, Ramchandran K, Moulin P (1999) Low-complexity image denoising based on statistical modeling of wavelet coefficients. IEEE Signal Process Lett 6(12):300–303CrossRefGoogle Scholar
  44. 44.
    Rosa AD, Uccheddu F, Costanzo A, Piva A, Barni M (2010) Exploring image dependencies: a new challange in image forensics. In: Memon N, Dittmann J, Alattar A (eds) Proceeedings of SPIE electronic media forensics and security XII, vol 7541, pp 0X-1–0X-12Google Scholar
  45. 45.
    Rosenfeld K, Sencar HT (2009) A study of the robustness of PRNU-based camera identification. In : Memon N, Dittmann J, Alattar A (eds) Proceedings of SPIE electronic imaging, steganography, security, and watermarking of multimedia contents XI, vol 7254, pp 0M–0NGoogle Scholar
  46. 46.
    Steinebach M, Liu H, Fan P, Katzenbeisser S (2010) Cell phone camera ballistics: attacks and countermeasures. In: Proceedings of SPIE, multimedia on mobile devices 2010, vol 7542, San Jose CA, pp 0B–0CGoogle Scholar
  47. 47.
    Sutcu Y, Bayram S, Sencar HT, Memon N (2007) Improvements on sensor noise based source camera identification. In: IEEE international conference on multimedia and expo, pp 24–27Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.T. J. Watson School of Applied Science and Engineering, SUNY BinghamtonBinghamtonUSA

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