A Preliminary Approach to Using PRNU Based Transfer Learning for Camera Identification

Seshadri, Sharan; Akshatha, K. R.; Karunakar, A. K.; Paul, Kelvin Harrison

doi:10.1007/978-3-030-17798-0_21

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…Recently, some studies have been reported about forensic methods related to the matching of source digital cameras to disputed digital images. e PRNU-based methods use correlation measures such as cross-correlation, peak-to-correlation energy, and correlation predictors [21], or they use filtering techniques [22] that use artificial neural networks and some artifacts introduced into digital images by the JPEG compression [13,23]. ere are several methods that verify their effectiveness by considering that the disputed images are flat images.…”

Section: Introductionmentioning

confidence: 99%

Identifying the Digital Camera from Natural Images Using Residual Noise and the Jensen–Shannon Divergence

Rodríguez-Santos

Reséndiz

Delgado-Gutiérrez

et al. 2022

Journal of Electrical and Computer Engineering

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Regarding the problem of digital camera identification, many methods have been proposed, and for several of them, their effectiveness has been verified on the basis of disputed flat images. However, in real cases the disputed images are natural images, rather than flat images. In that case, several of the already proposed methods are not effective. Hence, in this paper, a method is proposed for the digital camera identification from natural images based on the statistical comparison between the residual noise in the natural disputed images and the fingerprint defined for the eligible digital cameras. In the reported case studies, the HDR database provided by the Communications and Signal Processing Laboratory of University of Florence is used to select a set of eligible digital cameras, and from this image database, for each digital camera, a set of disputed flat images, a set of disputed natural images, and a set of flat reference images were selected. Thus, the fingerprint of each digital camera was calculated from the probability density function (PDF) of the photo-response nonuniformity (PRNU) extracted from its reference images. Therefore, in order to identify the source digital camera of a natural disputed image, the Jensen–Shannon divergence (JSD) was implemented to statistically compare the PRNU-based fingerprint of each eligible source camera against the noise residual of that disputed image. The proposed method has a similar effectiveness to methods based on the peak-to-correlation energy or the Kullback–Leibler divergence when the disputed images are flat images and the PRNU is considered, but it is significantly more effective than those methods when the disputed images are natural images.

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