Capture device identification from digital images using Kullback-Leibler divergence

It is proposed a forensic method for the capture device identification from digital images, which requires two elements: i) a digital image subject to controversy named disputed image and ii) a set of eligible capture devices with which the disputed image could have been shot. In order to define a d...

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Vydané v:	Multimedia tools and applications Ročník 80; číslo 13; s. 19513 - 19538
Hlavní autori:	Quintanar-Reséndiz, Ana L., Rodríguez-Santos, Francisco, Pichardo-Méndez, Josué L., Delgado-Gutiérrez, Guillermo, Ramírez, Omar Jiménez, Vázquez-Medina, Rubén
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.05.2021 Springer Nature B.V
Predmet:	Case studies Computer Communication Networks Computer Science Data Structures and Information Theory Digital imaging Fingerprints Forensic sciences Multimedia Information Systems Nonuniformity Probability distribution Signal processing Special Purpose and Application-Based Systems Photographic cameras (07.68.+m) Statistical mechanics (05.20.-y) Forensic science (89.20.Mn) Image processing algorithms (07.05.Pj) Statistics (02.50.-r)
ISSN:	1380-7501, 1573-7721
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Abstract	It is proposed a forensic method for the capture device identification from digital images, which requires two elements: i) a digital image subject to controversy named disputed image and ii) a set of eligible capture devices with which the disputed image could have been shot. In order to define a device statistical fingerprint , a set of reference digital images is produced for each eligible capture device. The device statistical fingerprint is estimated averaging the statistical distribution of the photo response non-uniformity (PRNU) signal extracted from each set of reference digital images. Then, a comparison based on Kullback-Leibler divergence (KLD) is performed between the statistical fingerprint for each capture device and the statistical distribution of the PRNU signal extracted from the disputed image . Considering that KLD is a non-symmetric measure, the capture device, for which the smallest KLD has been estimated, will be chosen such as the one that shot the disputed image. The effectiveness of the proposed method was estimated by using a case study, which includes eight eligible capture devices, each of which shot thirty reference images and twenty disputed images. Then, the performance of the proposed method was like the performance of the methods that use peak-to-correlation energy as the discrimination criterion when they were applied to the case study. Finally, the proposed method offers two advantages; it reduces the processing time when the PRNU signal is extracted from digital image and it avoids the aberration produced by the lens into the PRNU signal.
AbstractList	It is proposed a forensic method for the capture device identification from digital images, which requires two elements: i) a digital image subject to controversy named disputed image and ii) a set of eligible capture devices with which the disputed image could have been shot. In order to define a device statistical fingerprint, a set of reference digital images is produced for each eligible capture device. The device statistical fingerprint is estimated averaging the statistical distribution of the photo response non-uniformity (PRNU) signal extracted from each set of reference digital images. Then, a comparison based on Kullback-Leibler divergence (KLD) is performed between the statistical fingerprint for each capture device and the statistical distribution of the PRNU signal extracted from the disputed image. Considering that KLD is a non-symmetric measure, the capture device, for which the smallest KLD has been estimated, will be chosen such as the one that shot the disputed image. The effectiveness of the proposed method was estimated by using a case study, which includes eight eligible capture devices, each of which shot thirty reference images and twenty disputed images. Then, the performance of the proposed method was like the performance of the methods that use peak-to-correlation energy as the discrimination criterion when they were applied to the case study. Finally, the proposed method offers two advantages; it reduces the processing time when the PRNU signal is extracted from digital image and it avoids the aberration produced by the lens into the PRNU signal. It is proposed a forensic method for the capture device identification from digital images, which requires two elements: i) a digital image subject to controversy named disputed image and ii) a set of eligible capture devices with which the disputed image could have been shot. In order to define a device statistical fingerprint , a set of reference digital images is produced for each eligible capture device. The device statistical fingerprint is estimated averaging the statistical distribution of the photo response non-uniformity (PRNU) signal extracted from each set of reference digital images. Then, a comparison based on Kullback-Leibler divergence (KLD) is performed between the statistical fingerprint for each capture device and the statistical distribution of the PRNU signal extracted from the disputed image . Considering that KLD is a non-symmetric measure, the capture device, for which the smallest KLD has been estimated, will be chosen such as the one that shot the disputed image. The effectiveness of the proposed method was estimated by using a case study, which includes eight eligible capture devices, each of which shot thirty reference images and twenty disputed images. Then, the performance of the proposed method was like the performance of the methods that use peak-to-correlation energy as the discrimination criterion when they were applied to the case study. Finally, the proposed method offers two advantages; it reduces the processing time when the PRNU signal is extracted from digital image and it avoids the aberration produced by the lens into the PRNU signal.
Author	Delgado-Gutiérrez, Guillermo Quintanar-Reséndiz, Ana L. Vázquez-Medina, Rubén Rodríguez-Santos, Francisco Ramírez, Omar Jiménez Pichardo-Méndez, Josué L.
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Snippet	It is proposed a forensic method for the capture device identification from digital images, which requires two elements: i) a digital image subject to...
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StartPage	19513
SubjectTerms	Case studies Computer Communication Networks Computer Science Data Structures and Information Theory Digital imaging Fingerprints Forensic sciences Multimedia Information Systems Nonuniformity Probability distribution Signal processing Special Purpose and Application-Based Systems
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Title	Capture device identification from digital images using Kullback-Leibler divergence
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Volume	80
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