Color processing in digital cameras

Adams, James E.; Parulski, Kenneth A.; Spaulding, Kevin E.

doi:10.1109/40.743681

Cited by 205 publications

(94 citation statements)

References 5 publications

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“…The basic structure of a digital camera pipeline is shown in Figure 1-(a) [7]. After light enters the camera through the lens, a set of filters are employed, the most important being an anti-aliasing filter.…”

Section: Image Formation In Digital Camerasmentioning

confidence: 99%

Source camera identification based on CFA interpolation

Sevinç

Sencar²,

Memon³

et al. 2005

IEEE International Conference on Image Processing 2005

267

153

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In this work, we focus our interest on blind source camera identification problem by extending our results in the direction of [1]. The interpolation in the color surface of an image due to the use of a color filter array (CFA) forms the basis of the paper. We propose to identify the source camera of an image based on traces of the proprietary interpolation algorithm deployed by a digital camera. For this purpose, a set of image characteristics are defined and then used in conjunction with a support vector machine based multi-class classifier to determine the originating digital camera. We also provide initial results on identifying source among two and three digital cameras.

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Section: Image Formation In Digital Camerasmentioning

confidence: 99%

Source camera identification based on CFA interpolation

Sevinç

Sencar²,

Memon³

et al. 2005

IEEE International Conference on Image Processing 2005

267

153

View full text Add to dashboard Cite

show abstract

“…Digital cameras consist of lens system, filters, color filter array (CFA), image sensor, and digital image processor (DIP) [9]. Color images may suffer from aberrations caused by the lenses, such as chromatic aberration and spherical aberration.…”

Section: Inside a Digital Cameramentioning

confidence: 99%

A Survey on Digital Camera Image Forensic Methods

Lanh

Chong

Emmanuel

et al. 2007

Multimedia and Expo, 2007 IEEE International Conference On

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There are two main interests in Image Forensics, namely source identification and forgery detection. In this paper, we first briefly provide an introduction to the major processing stages inside a digital camera and then review several methods for source digital camera identification and forgery detection. Existing methods for source identification explore the various processing stages inside a digital camera to derive the clues for distinguishing the source cameras while forgery detection checks for inconsistencies in image quality or for presence of certain characteristics as evidence of tampering.

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“…Although the color image construction process may vary extensively within different makes ofdigital camcras [2], however, it is our helief that the output image is cffcctcd greatly by the following two components:…”

Section: Identifying Measuresmentioning

confidence: 99%

Blind source camera identification

Kharrazi¹,

Sencar²,

Memon³

2004 International Conference on Image Processing, 2004. ICIP '04.

213

162

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An interesting prohlem in digital forensics is that given a digital image, would it he possihle to identify the camera model which was used to obtain the image. In this paper we look at a simplified version of this problem by trying to distinguish between images captured by a limited number of camera models. We proposc i number of features which could be used by a classifier to idcntify the source camera of an image in a blind manner. We also provide experimental results and show reasonable accuracy in distinguishing images from the two and five different camera models using the proposed features.

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Color processing in digital cameras

Cited by 205 publications

References 5 publications

Source camera identification based on CFA interpolation

Source camera identification based on CFA interpolation

A Survey on Digital Camera Image Forensic Methods

Blind source camera identification

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