NDLI: Source Camera Identification for Low Resolution Heavily Compressed Images

Content Provider	IEEE Xplore Digital Library
Author	Alles, E.J. Geradts, Z.J.M.H. Veenman, C.J.
Copyright Year	2008
Description	Author affiliation: Delft Univ. of Technol., Delft (Alles, E.J.)
Abstract	In this paper, we propose a method to exploit photo response non-uniformity (PRNU) to identify the source camera of heavily JPEG compressed digital photographs of resolution 640 times 480 pixels. Similarly to research reported previously, we extract the PRNU patterns from both reference and questioned images using a two-dimensional high-pass filter and compare these patterns by calculating the correlation coefficient between them. To deal with the low quality compressed image material, we propose a simple and effective way to obtain the PRNU. We did extensive experiments for both the closed and open set source camera identification problem with a set of 38 cameras of four different types. For the closed set problem accuracies as high as 83% for single images and 100% for around 20 simultaneously identified questioned images were obtained. For the open set problem, decision levels were obtained for several numbers of simultaneously identified questioned images. The corresponding false rejection rates were unsatisfactory for single images, but improved substantially for simultaneous identification of multiple questioned images.
Starting Page	557
Ending Page	567
File Size	567007
Page Count	11
File Format	PDF
ISBN	9780769532431
DOI	10.1109/ICCSA.2008.18
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-30
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image resolution Image coding Digital cameras Pixel Transform coding Digital images Forensics Data mining Image sensors Q factor digital fingerprint Forensic Science digital source camera identification fixed pattern noise JPEG compression
Content Type	Text
Resource Type	Article

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