Radon transform of image monotonic rearrangements as feature for noise sensor signature

Vittoria Bruni, Silvia Marconi, Giuseppina Monteverde and Domenico Vitulano

Applied Mathematics and Computation, 2023, vol. 457, issue C

Abstract: Source camera identification represents a delicate, crucial but challenging task in digital forensics, especially when an image has to be used as a proof in a court of law. This paper investigates some properties of the Photo Response Non Uniformity (PRNU) pattern noise that represents the fingerprint of any acquisition sensor. The main goal is to define specific and distinctive features for this noise source that enable the identification of the acquisition sensor by simply analysing a few images. These features are required to be independent of image size, modifications, storage mode, etc. The discrimination power of the decreasing rearrangement of a function, combined with the Radon transform, has been investigated in this paper. Preliminary tests show that a proper rearrangement of PRNU image provides specific and device-dependent geometric structures that can be properly coded through the Radon transform. In particular, the empirical distribution of the Radon Transform of rearranged Flat Field images alone is capable to correctly characterize each device with high accuracy, showing robusteness to some standard image modifications, such as quantization and blurring; in addition, it guarantees independence of image size.

Keywords: Radon transform; Function monotonic rearrangements; Source camera identification; PRNU (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:457:y:2023:i:c:s0096300323003429

DOI: 10.1016/j.amc.2023.128173

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