Two-degree of freedom Mahalanobis classifier for smartphone-camera identification from natural digital images
The portability and popularity of smartphones makes it easy to capture digital images in a variety of situations, including witnessing criminal activity. Forensic analysis of digital images captured by smartphone-cameras could be used for legal and investigative purposes, not only to have a recordin...
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| Format: | Article |
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PeerJ Inc.
2024-12-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2513.pdf |
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| author | Rubén Vázquez-Medina César Enrique Rojas-López Omar Jiménez-Ramírez Luis Niño-de-Rvera-Oyarzabal Leonardo Palacios-Luengas |
| author_facet | Rubén Vázquez-Medina César Enrique Rojas-López Omar Jiménez-Ramírez Luis Niño-de-Rvera-Oyarzabal Leonardo Palacios-Luengas |
| author_sort | Rubén Vázquez-Medina |
| collection | DOAJ |
| description | The portability and popularity of smartphones makes it easy to capture digital images in a variety of situations, including witnessing criminal activity. Forensic analysis of digital images captured by smartphone-cameras could be used for legal and investigative purposes, not only to have a recording of an act, but also to establish a relationship between a digital image and its capture device, and between the latter and a person. Fortunately, given the similarities, forensic ballistics techniques and procedures used to identify weapons from fired bullets can be used to identify smartphone-cameras from digital images. However, while there are several solutions for identifying smartphone-cameras from digital images, not all of them focus on two key issues: reducing the number of reference images used to create the fingerprint of the smartphone-camera and reducing the processing time for identification. To address these issues, a method based on a two-degree-of-freedom discriminant analysis using pixel intensity and intrinsic noise in digital images is proposed. It uses a Mahalanobis classifier to compare the traces left by the capture source in a digital image with the fingerprints calculated for the candidate smartphone-cameras. This allows the identification of the most likely smartphone-camera that captured a digital image. A significant advantage of the proposed method is that it relies on a smaller number of reference images to estimate the smartphone-camera fingerprints. They are built using only fifteen reference images, as opposed to thirty or more images required by other techniques. This means faster processing times as image clippings are analyzed rather than whole digital images. The proposed method demonstrates high performance, since for disputed flat images it achieves an identification effectiveness rate of 87.50% with one reference image, and 100.00% when fifteen reference images are considered. For disputed natural images, it achieves an identification effectiveness rate of 97.50% with fifteen reference images. |
| format | Article |
| id | doaj-art-541be3f81e7a4656be2fb86eaf244a1e |
| institution | DOAJ |
| issn | 2376-5992 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-541be3f81e7a4656be2fb86eaf244a1e2025-08-20T02:39:51ZengPeerJ Inc.PeerJ Computer Science2376-59922024-12-0110e251310.7717/peerj-cs.2513Two-degree of freedom Mahalanobis classifier for smartphone-camera identification from natural digital imagesRubén Vázquez-Medina0César Enrique Rojas-López1Omar Jiménez-Ramírez2Luis Niño-de-Rvera-Oyarzabal3Leonardo Palacios-Luengas4Instituto Politécnico Nacional, CICATA Querétaro, Santiago de Querétaro, Querétaro, MexicoInstituto Politécnico Nacional, ESIME Culhuacan, Ciudad de México, MexicoInstituto Politécnico Nacional, ESIME Culhuacan, Ciudad de México, MexicoInstituto Politécnico Nacional, ESIME Culhuacan, Ciudad de México, MexicoDepartment of Electrical Engineering, Autonomous Metropolitan University, Iztapalapa, Ciudad de México, MexicoThe portability and popularity of smartphones makes it easy to capture digital images in a variety of situations, including witnessing criminal activity. Forensic analysis of digital images captured by smartphone-cameras could be used for legal and investigative purposes, not only to have a recording of an act, but also to establish a relationship between a digital image and its capture device, and between the latter and a person. Fortunately, given the similarities, forensic ballistics techniques and procedures used to identify weapons from fired bullets can be used to identify smartphone-cameras from digital images. However, while there are several solutions for identifying smartphone-cameras from digital images, not all of them focus on two key issues: reducing the number of reference images used to create the fingerprint of the smartphone-camera and reducing the processing time for identification. To address these issues, a method based on a two-degree-of-freedom discriminant analysis using pixel intensity and intrinsic noise in digital images is proposed. It uses a Mahalanobis classifier to compare the traces left by the capture source in a digital image with the fingerprints calculated for the candidate smartphone-cameras. This allows the identification of the most likely smartphone-camera that captured a digital image. A significant advantage of the proposed method is that it relies on a smaller number of reference images to estimate the smartphone-camera fingerprints. They are built using only fifteen reference images, as opposed to thirty or more images required by other techniques. This means faster processing times as image clippings are analyzed rather than whole digital images. The proposed method demonstrates high performance, since for disputed flat images it achieves an identification effectiveness rate of 87.50% with one reference image, and 100.00% when fifteen reference images are considered. For disputed natural images, it achieves an identification effectiveness rate of 97.50% with fifteen reference images.https://peerj.com/articles/cs-2513.pdfSmartphone-camera fingerprintsDigital camera patternMahalanobis classifierCamera-in-image traces |
| spellingShingle | Rubén Vázquez-Medina César Enrique Rojas-López Omar Jiménez-Ramírez Luis Niño-de-Rvera-Oyarzabal Leonardo Palacios-Luengas Two-degree of freedom Mahalanobis classifier for smartphone-camera identification from natural digital images PeerJ Computer Science Smartphone-camera fingerprints Digital camera pattern Mahalanobis classifier Camera-in-image traces |
| title | Two-degree of freedom Mahalanobis classifier for smartphone-camera identification from natural digital images |
| title_full | Two-degree of freedom Mahalanobis classifier for smartphone-camera identification from natural digital images |
| title_fullStr | Two-degree of freedom Mahalanobis classifier for smartphone-camera identification from natural digital images |
| title_full_unstemmed | Two-degree of freedom Mahalanobis classifier for smartphone-camera identification from natural digital images |
| title_short | Two-degree of freedom Mahalanobis classifier for smartphone-camera identification from natural digital images |
| title_sort | two degree of freedom mahalanobis classifier for smartphone camera identification from natural digital images |
| topic | Smartphone-camera fingerprints Digital camera pattern Mahalanobis classifier Camera-in-image traces |
| url | https://peerj.com/articles/cs-2513.pdf |
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