  Performance of the sensor-based camera identification (SCI) method heavily
relies on the denoising filter in estimating Photo-Response Non-Uniformity
(PRNU). Given various attempts on enhancing the quality of the extracted PRNU,
it still suffers from unsatisfactory performance in low-resolution images and
high computational demand. Leveraging the similarity of PRNU estimation and
image denoising, we take advantage of the latest achievements of Convolutional
Neural Network (CNN)-based denoisers for PRNU extraction. In this paper, a
comparative evaluation of such CNN denoisers on SCI performance is carried out
on the public "Dresden Image Database". Our findings are two-fold. From one
aspect, both the PRNU extraction and image denoising separate noise from the
image content. Hence, SCI can benefit from the recent CNN denoisers if
carefully trained. From another aspect, the goals and the scenarios of PRNU
extraction and image denoising are different since one optimizes the quality of
noise and the other optimizes the image quality. A carefully tailored training
is needed when CNN denoisers are used for PRNU estimation. Alternative
strategies of training data preparation and loss function design are analyzed
theoretically and evaluated experimentally. We point out that feeding the CNNs
with image-PRNU pairs and training them with correlation-based loss function
result in the best PRNU estimation performance. To facilitate further studies
of SCI, we also propose a minimum-loss camera fingerprint quantization scheme
using which we save the fingerprints as image files in PNG format. Furthermore,
we make the quantized fingerprints of the cameras from the "Dresden Image
Database" publicly available.
