Digital single lens reflex camera identification from traces of sensor dust

Ahmet Emir Dirik; Husrev Taha Senear; Nasir Memon

doi:10.1109/TIFS.2008.926987

Digital single lens reflex camera identification from traces of sensor dust

Ahmet Emir Dirik, Husrev Taha Senear, Nasir Memon

New York University

Research output: Contribution to journal › Article › peer-review

135 Citations (Scopus)

Abstract

Digital single lens reflex cameras suffer from a well-known sensor dust problem due to interchangeable lenses that they deploy. The dust particles that settle in front of the imaging sensor create a persistent pattern in all captured images. In this paper, we propose a novel source camera identification method based on detection and matching of these dust-spot characteristics. Dust spots in the image are detected based on a (Gaussian) intensity loss model and shape properties. To prevent false detections, lens parameter-dependent characteristics of dust spots are also taken into consideration. Experimental results show that the proposed detection scheme can be used in identification of the source digital single lens reflex camera at low false positive rates, even under heavy compression and downsampling.

Original language	English
Article number	4558161
Pages (from-to)	539-552
Number of pages	14
Journal	IEEE Transactions on Information Forensics and Security
Volume	3
Issue number	3
DOIs	https://doi.org/10.1109/TIFS.2008.926987
Publication status	Published - Sept 2008
Externally published	Yes

Keywords

Digital forensics
Digital single lens reflex (DSLR)
Sensor dust

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10.1109/TIFS.2008.926987

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title = "Digital single lens reflex camera identification from traces of sensor dust",

abstract = "Digital single lens reflex cameras suffer from a well-known sensor dust problem due to interchangeable lenses that they deploy. The dust particles that settle in front of the imaging sensor create a persistent pattern in all captured images. In this paper, we propose a novel source camera identification method based on detection and matching of these dust-spot characteristics. Dust spots in the image are detected based on a (Gaussian) intensity loss model and shape properties. To prevent false detections, lens parameter-dependent characteristics of dust spots are also taken into consideration. Experimental results show that the proposed detection scheme can be used in identification of the source digital single lens reflex camera at low false positive rates, even under heavy compression and downsampling.",

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N2 - Digital single lens reflex cameras suffer from a well-known sensor dust problem due to interchangeable lenses that they deploy. The dust particles that settle in front of the imaging sensor create a persistent pattern in all captured images. In this paper, we propose a novel source camera identification method based on detection and matching of these dust-spot characteristics. Dust spots in the image are detected based on a (Gaussian) intensity loss model and shape properties. To prevent false detections, lens parameter-dependent characteristics of dust spots are also taken into consideration. Experimental results show that the proposed detection scheme can be used in identification of the source digital single lens reflex camera at low false positive rates, even under heavy compression and downsampling.

AB - Digital single lens reflex cameras suffer from a well-known sensor dust problem due to interchangeable lenses that they deploy. The dust particles that settle in front of the imaging sensor create a persistent pattern in all captured images. In this paper, we propose a novel source camera identification method based on detection and matching of these dust-spot characteristics. Dust spots in the image are detected based on a (Gaussian) intensity loss model and shape properties. To prevent false detections, lens parameter-dependent characteristics of dust spots are also taken into consideration. Experimental results show that the proposed detection scheme can be used in identification of the source digital single lens reflex camera at low false positive rates, even under heavy compression and downsampling.

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Digital single lens reflex camera identification from traces of sensor dust

Abstract

Keywords

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