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Volume: 33 | Article ID: art00012
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Camera Fingerprint estimation with a Generative Adversarial Network (GAN)
  DOI :  10.2352/ISSN.2470-1173.2021.4.MWSF-336  Published OnlineJanuary 2021
Abstract

For forensic analysis of digital images or videos, the PRNU or camera fingerprint is the most important characteristics, for source attribution and manipulation localization. Typically, a good estimate of the PRNU is obtained by computing its Maximum Likelihood estimate from noise residuals of a large number of flatfield images captured by the camera. In this paper, we propose a novel approach of estimating the fingerprint of a camera, with a Generative Adversarial Network (GAN). The idea is to let the Generator network learn a distribution, from which PRNU samples will be drawn after training of the two adversarial networks. Experimental results indicate that the GAN-generated PRNU yields state-of-the-art camera identification and manipulation localization results.

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Sujoy Chakraborty, "Camera Fingerprint estimation with a Generative Adversarial Network (GAN)in Proc. IS&T Int’l. Symp. on Electronic Imaging: Media Watermarking, Security, and Forensics,  2021,  pp 336-1 - 336-9,  https://doi.org/10.2352/ISSN.2470-1173.2021.4.MWSF-336

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