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2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Digital camera identification based on curvelet transform
Taipei, Taiwan
April 19-April 24
ISBN: 978-1-4244-2353-8
| ASCII Text | x | ||
| Chi Zhang, Hongbin Zhang, "Digital camera identification based on curvelet transform," Acoustics, Speech, and Signal Processing, IEEE International Conference on, pp. 1389-1392, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009. | |||
| BibTex | x | ||
| @article{ 10.1109/ICASSP.2009.4959852, author = {Chi Zhang and Hongbin Zhang}, title = {Digital camera identification based on curvelet transform}, journal ={Acoustics, Speech, and Signal Processing, IEEE International Conference on}, volume = {0}, year = {2009}, isbn = {978-1-4244-2353-8}, pages = {1389-1392}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICASSP.2009.4959852}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Acoustics, Speech, and Signal Processing, IEEE International Conference on TI - Digital camera identification based on curvelet transform SN - 978-1-4244-2353-8 SP1389 EP1392 A1 - Chi Zhang, A1 - Hongbin Zhang, PY - 2009 VL - 0 JA - Acoustics, Speech, and Signal Processing, IEEE International Conference on ER - | |||
In this paper, A new method is proposed for digital camera identification from its color images using image sensor noise. Currently the proposed camera identification methods use wavelet-based denoising filter to extract the sensor noise feature. However, the wavelet methods may smooth the edged while denoising and this will lead to low accuracy for those images including highly textured regions. In order to overcome some inherent limitations of wavelet transform, we use curvelet-based denoising filter to obtain the camera fingerprint. Experimental results show that this method provides higher accuracy than other methods on the condition of using a few color images to compute reference pattern, especially for those color images including highly textured regions.
Citation:
Chi Zhang, Hongbin Zhang, "Digital camera identification based on curvelet transform," icassp, pp.1389-1392, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009
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