A reference estimator based on composite sensor pattern noise for source device identification

Ruizhe Li; Chang-Tsun Li; Yu Guan

doi:10.1117/12.2038651

19 February 2014 A reference estimator based on composite sensor pattern noise for source device identification

Ruizhe Li, Chang-Tsun Li, Yu Guan

Proceedings Volume 9028, Media Watermarking, Security, and Forensics 2014; 90280O (2014) https://doi.org/10.1117/12.2038651
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States

Abstract

It has been proved that Sensor Pattern Noise (SPN) can serve as an imaging device fingerprint for source camera identification. Reference SPN estimation is a very important procedure within the framework of this application. Most previous works built reference SPN by averaging the SPNs extracted from 50 images of blue sky. However, this method can be problematic. Firstly, in practice we may face the problem of source camera identification in the absence of the imaging cameras and reference SPNs, which means only natural images with scene details are available for reference SPN estimation rather than blue sky images. It is challenging because the reference SPN can be severely contaminated by image content. Secondly, the number of available reference images sometimes is too few for existing methods to estimate a reliable reference SPN. In fact, existing methods lack consideration of the number of available reference images as they were designed for the datasets with abundant images to estimate the reference SPN. In order to deal with the aforementioned problem, in this work, a novel reference estimator is proposed. Experimental results show that our proposed method achieves better performance than the methods based on the averaged reference SPN, especially when few reference images used.

Citation Download Citation

Ruizhe Li, Chang-Tsun Li, and Yu Guan "A reference estimator based on composite sensor pattern noise for source device identification", Proc. SPIE 9028, Media Watermarking, Security, and Forensics 2014, 90280O (19 February 2014); https://doi.org/10.1117/12.2038651

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