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Source camera identification using enhanced sensor pattern noise

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Li, Chang-Tsun. (2010) Source camera identification using enhanced sensor pattern noise. IEEE Transactions on Information Forensics and Security, Vol.5 (No.2). pp. 280-287. ISSN 1556-6013

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Official URL: http://dx.doi.org/10.1109/TIFS.2010.2046268

Abstract

Sensor pattern noises (SPNs), extracted from digital images to serve as the fingerprints of imaging devices, have been proved as an effective way for digital device identification. However, as we demonstrate in this work, the limitation of the current method of extracting SPNs is that the SPNs extracted from images can be severely contaminated by details from scenes, and as a result, the identification rate is unsatisfactory unless images of a large size are used. In this work, we propose a novel approach for attenuating the influence of details from scenes on SPNs so as to improve the device identification rate of the identifier. The hypothesis underlying our SPN enhancement method is that the stronger a signal component in an SPN is, the less trustworthy the component should be, and thus should be attenuated. This hypothesis suggests that an enhanced SPN can be obtained by assigning weighting factors inversely proportional to the magnitude of the SPN components.

Item Type:

Journal Article

Subjects:

T Technology > TR Photography

Divisions:

Faculty of Science > Computer Science

Library of Congress Subject Headings (LCSH):

Digital images, Digital cameras -- Identification, Images, Photographic -- Noise, Image processing

Journal or Publication Title:

IEEE Transactions on Information Forensics and Security

Publisher:

IEEE

ISSN:

1556-6013

Official Date:

June 2010

Dates:

Date	Event
June 2010	Published

Volume:

Vol.5

Number:

No.2

Page Range:

pp. 280-287

Identification Number:

10.1109/TIFS.2010.2046268

Status:

Peer Reviewed

Access rights to Published version:

Open Access

Funder:

Forensic Pathways (Firm)

References:

[1] J. Lukáˇs, J. Fridrich, and M. Goljan, “Digital camera identification from
sensor pattern noise,” IEEE Trans. Inf. Forensics Security, vol. 1, no.
2, pp. 205–214, Jun. 2006.
[2] M. Goljan, M. Chen, and J. Fridrich, “Identifying common source digital
camera from image pairs,” in Proc. IEEE Int. Conf. Image Processing,
San Antonio, TX, Sep. 2007, pp. 14–19.
[3] Y. Sutcu, S. Batram, H. T. Sencar, and N. Memon, “Improvements on
sensor noise based source camera identification,” in Proc. IEEE Int.
Conf. Multimedia and Expo, Beijin, China, Jul. 2–5, 2007, pp. 24–27.
[4] R. Caldelli, I. Amerini, F. Picchioni, A. De Rosa, and F. Uccheddu,
“Multimedia forensic techniques for acquisition device identification
and digital image authentication,” in Handbook of Research on Computational
Forensics, Digital Crime and Investigation: Methods and
Solutions, C.-T. Li, Ed. Hershey, PA: Information Science Reference
(IGI Global), Nov. 2009.
[5] R. Caldelli, I. Amerini, and F. Picchioni, “Distinguishing between
camera and scanned images by means of frequency analysis,” Int. J.
Digital Crime Forensics, vol. 2, no. 1, Jan./Mar. 2010.
[6] M. Chen, J. Fridrich, M. Goljan, and J. Lukáˇs, “Determining image
origin and integrity using sensor noise,” IEEE Trans. Inf. Forensics Security,
vol. 3, no. 1, pp. 74–90, Mar. 2008.
[7] C.-T. Li, “Methods for Identifying Imaging Devices and Classifying
Images Acquired by Unknown Imaging Devices,” U.K. Patent Pending,
Application 0902406.5.
[8] N. Khanna, G. T.-C. Chiu, J. P. Allebach, and E. J. Delp, “Forensic
techniques for classifying scanner, computer generated and digital
camera images,” in Proc. IEEE Int. Conf. Acoustics, Speech, and
Signal Processing, Las Vegas, NV, Mar. 30–Apr. 4 2008.
[9] Y. F. Hsu and S. F. Chang, “Image splicing detection using camera
response function consistency and automatic segmentation,” in Proc.
IEEE Int. Conf. Multimedia and Expo, Beijin, China, Jul. 2–5, 2007.
[10] A. C. Popescu and H. Farid, “Exposing digital forgeries by detecting
traces of resampling,” IEEE Trans. Signal Process., vol. 53, no. 2, pt.
2, pp. 758–767, Feb. 2005.
[11] A. C. Popescu and H. Farid, “Exposing digital forgeries in color filter
array interpolated images,” IEEE Trans. Signal Process., vol. 53, no.
10, pp. 3948–3959, Oct. 2005.
[12] A. Swaminathan, M. Wu, and K. J. R. Liu, “Nonintrusive component
forensics of visual sensors using output images,” IEEE Trans. Inf.
Forensics Security, vol. 2, no. 1, pp. 91–106, Mar. 2007.
[13] M. J. Sorell, “Digital camera source identification through JPEG quantisation,”
in Multimedia Forensics and Security, C.-T. Li, Ed. Hershey,
PA: Information Science Reference (IGI Global), 2008.
[14] M. J. Sorell, “Conditions for effective detection and identification of
primary quantisation of re-quantized JPEG images,” Int. J. Digital
Crime Forensics, vol. 1, no. 2, pp. 13–27, Apr./Jun. 2009.
[15] S. Choi, E. Y. Lam, and K. K. Y.Wong, “Source camera identification
using footprints from lens aberration,” Proc. SPIE, 2006.
[16] V. T. Lanh, S. Emmanuel, and M. S. Kankanhalli, “Identifying source
cell phone using chromatic aberration,” in Proc. IEEE Conf. Multimedia
and Expo, Beijin, China, Jul. 2–5, 2007.
[17] B. Sankur, O. Celiktutan, and I. Avcibas, “Blind identification of
cell phone cameras,” in Proc. SPIE, Electronic Imaging, Security,
Steganography, and Watermarking of Multimedia Contents IX, San
Jose, CA, Jan. 29–Feb. 1 2007, vol. 6505, pp. 1H–1I.
[18] G. Xu, S. Gao, Y. Q. Shi, W. Su, and R. Hu, “Camera-model identification
using markovian transition probability matrix,” in Proc. Int. Workshop
on DigitalWatermarking, Guildford, U.K., Aug. 24–26, 2009, pp.
294–307.
[19] P. Sutthiwan, J. Ye, and Y. Q. Shi, “An enhanced statistical approach
to identifying photorealistic images,” in Proc. Int.Workshop on Digital
Watermarking, Guildford, U.K., Aug. 24–26, 2009, pp. 323–335.
[20] S. Bayram, H. T. Sencar, and N. Memon, “Video copy detection based
on source device characteristics: A complementary approach to content-
based methods,” in Proc. First ACM Int. Conf. Multimedia Information
Retrieval, Vancouver, Canada, Oct. 30–31, 2008, pp. 435–442.
[21] H. Gou, A. Swaminathan, and M. Wu, “Intrinsic sensor noise features
for forensic analysis on scanners and scanned images,” IEEE Trans.
Inf. Forensics Security, vol. 4, no. 3, pp. 476–491, Sep. 2009.
[22] J. Fridrich, “Digital image forensic using sensor noise,” IEEE Signal
Process. Mag., vol. 26, no. 2, pp. 26–37, Mar. 2009.
[23] J. R. Janesick, Scientific Charge-Coupled Devices. Bellingham,WA:
SPIE, 2001, vol. PM83.
[24] T. Yamada, “CCD image sensors,” in Image Sensors and Signal Processing
for Digital Still Cameras, J. Nakamura, Ed. NewYork: Taylor
& Francis, 2006.
[25] M. Chen, J. Fridrich, and M. Goljan, “Digital imaging sensor identification
(further study),” in Proc. SPIE Electronic Imaging, Jan. 2007,
Photonics West.