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Beyond PRNU: Learning Robust Device-Specific Fingerprint for Source Camera Identification

Source-camera identification tools assist image forensics investigators to associate an image with a camera. The Photo Response Non-Uniformity (PRNU) noise pattern caused by sensor imperfections has been proven to be an effective way to identify the source camera. However, the PRNU is susceptible to...

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Autores principales: Manisha, Li, Chang-Tsun, Lin, Xufeng, Kotegar, Karunakar A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9609198/
https://www.ncbi.nlm.nih.gov/pubmed/36298222
http://dx.doi.org/10.3390/s22207871
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author Manisha,
Li, Chang-Tsun
Lin, Xufeng
Kotegar, Karunakar A.
author_facet Manisha,
Li, Chang-Tsun
Lin, Xufeng
Kotegar, Karunakar A.
author_sort Manisha,
collection PubMed
description Source-camera identification tools assist image forensics investigators to associate an image with a camera. The Photo Response Non-Uniformity (PRNU) noise pattern caused by sensor imperfections has been proven to be an effective way to identify the source camera. However, the PRNU is susceptible to camera settings, scene details, image processing operations (e.g., simple low-pass filtering or JPEG compression), and counter-forensic attacks. A forensic investigator unaware of malicious counter-forensic attacks or incidental image manipulation is at risk of being misled. The spatial synchronization requirement during the matching of two PRNUs also represents a major limitation of the PRNU. To address the PRNU’s fragility issue, in recent years, deep learning-based data-driven approaches have been developed to identify source-camera models. However, the source information learned by existing deep learning models is not able to distinguish individual cameras of the same model. In light of the vulnerabilities of the PRNU fingerprint and data-driven techniques, in this paper, we bring to light the existence of a new robust data-driven device-specific fingerprint in digital images that is capable of identifying individual cameras of the same model in practical forensic scenarios. We discover that the new device fingerprint is location-independent, stochastic, and globally available, which resolves the spatial synchronization issue. Unlike the PRNU, which resides in the high-frequency band, the new device fingerprint is extracted from the low- and mid-frequency bands, which resolves the fragility issue that the PRNU is unable to contend with. Our experiments on various datasets also demonstrate that the new fingerprint is highly resilient to image manipulations such as rotation, gamma correction, and aggressive JPEG compression.
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spelling pubmed-96091982022-10-28 Beyond PRNU: Learning Robust Device-Specific Fingerprint for Source Camera Identification Manisha, Li, Chang-Tsun Lin, Xufeng Kotegar, Karunakar A. Sensors (Basel) Article Source-camera identification tools assist image forensics investigators to associate an image with a camera. The Photo Response Non-Uniformity (PRNU) noise pattern caused by sensor imperfections has been proven to be an effective way to identify the source camera. However, the PRNU is susceptible to camera settings, scene details, image processing operations (e.g., simple low-pass filtering or JPEG compression), and counter-forensic attacks. A forensic investigator unaware of malicious counter-forensic attacks or incidental image manipulation is at risk of being misled. The spatial synchronization requirement during the matching of two PRNUs also represents a major limitation of the PRNU. To address the PRNU’s fragility issue, in recent years, deep learning-based data-driven approaches have been developed to identify source-camera models. However, the source information learned by existing deep learning models is not able to distinguish individual cameras of the same model. In light of the vulnerabilities of the PRNU fingerprint and data-driven techniques, in this paper, we bring to light the existence of a new robust data-driven device-specific fingerprint in digital images that is capable of identifying individual cameras of the same model in practical forensic scenarios. We discover that the new device fingerprint is location-independent, stochastic, and globally available, which resolves the spatial synchronization issue. Unlike the PRNU, which resides in the high-frequency band, the new device fingerprint is extracted from the low- and mid-frequency bands, which resolves the fragility issue that the PRNU is unable to contend with. Our experiments on various datasets also demonstrate that the new fingerprint is highly resilient to image manipulations such as rotation, gamma correction, and aggressive JPEG compression. MDPI 2022-10-17 /pmc/articles/PMC9609198/ /pubmed/36298222 http://dx.doi.org/10.3390/s22207871 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Manisha,
Li, Chang-Tsun
Lin, Xufeng
Kotegar, Karunakar A.
Beyond PRNU: Learning Robust Device-Specific Fingerprint for Source Camera Identification
title Beyond PRNU: Learning Robust Device-Specific Fingerprint for Source Camera Identification
title_full Beyond PRNU: Learning Robust Device-Specific Fingerprint for Source Camera Identification
title_fullStr Beyond PRNU: Learning Robust Device-Specific Fingerprint for Source Camera Identification
title_full_unstemmed Beyond PRNU: Learning Robust Device-Specific Fingerprint for Source Camera Identification
title_short Beyond PRNU: Learning Robust Device-Specific Fingerprint for Source Camera Identification
title_sort beyond prnu: learning robust device-specific fingerprint for source camera identification
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9609198/
https://www.ncbi.nlm.nih.gov/pubmed/36298222
http://dx.doi.org/10.3390/s22207871
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