Capture device identification from digital images using Kullback-Leibler divergence

Reséndiz, Ana Laura Quintanar; Rodríguez-Santos, Francisco; Pichardo-Méndez, J. L.; Delgado-Gutiérrez, Guillermo; Ramírez, Omar; Vázquez-Medina, Rubén

doi:10.1007/s11042-021-10653-1

Cited by 9 publications

(23 citation statements)

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“…us, to define a PRNU-based statistical fingerprint for each eligible digital camera, firstly, it is assumed that there are different types of intrinsic signals in digital images: spatial noise, system noise, and temporal noise [17]. Spatial noise describes the variations of the intensities of different pixels due to the illumination considering homogeneous light.…”

Section: Methodsmentioning

confidence: 99%

“…In the above formula, S is the number of reference digital images of the eligible digital camera y∈ (1, C), R is the size of a regular partition in the definition interval for the PRNU extracted from each reference digital image x∈ (1, S), and μ i (S, y) is calculated in a similar way to Quintanar-Reséndiz et al in 2021 [17] (see (7)), considering that ρ i (x, y) is defined by (8), similar to Quintanar-Reséndiz et al in 2022 [38].…”

Section: Prnu-based Statistical Fingerprint For Digital Camerasmentioning

confidence: 99%

“…. ., R; m and n are, respectively, the number of rows and columns of each digital image x∈(1, S); and B Ai (u, v) denotes the belonging function of each PRNU component to the subinterval A i as expressed in the following equation similar to [17]:…”

Section: Prnu-based Statistical Fingerprint For Digital Camerasmentioning

confidence: 99%

“…ey consider as a fundamental premise that the noiseprint is a camera model fingerprint effective for several forensic tasks. In 2021, Quintanar-Reséndiz et al proposed a forensic algorithm based on the Kullback-Leibler divergence when identifying source digital cameras for digital images; they proposed a device statistical fingerprint and image noiseprint based on the PDF of the PRNU extracted from flat digital images [17]. On the contrary, in 2020, Taspinar et al investigated about source camera identification considering that digital images may have been captured using different resolutions and aspect ratios [18].…”

Section: Introductionmentioning

confidence: 99%

“…For example, in 2021, Albisani et al proposed a method that in addition to identifying devices leading to false attribution by the presence of NUAs (not unique artifacts), it also offers a solution of matching source digital cameras to flat disputed images [24]. Also, in 2021, Quintanar-Reséndiz et al proposed a method to capture device identification from digital images when the disputed images were flat images [17]. However, this condition has no meaning in practice since the disputed images in a real case are natural images.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%