The Library

Source camera identification using enhanced sensor pattern noise

Tools

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

PDF
WRAP_Li_Source_Camera.pdf - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (832Kb)

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
Date:	June 2010
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.
URI:	http://wrap.warwick.ac.uk/id/eprint/3318