Optical aberrations are an inherent part of all images captured with a digital camera and might form another valuable characteristic to build reliable image forensic methods. Within this paper we focus on vignetting and radial lens distortion and investigate interdependencies between both optical aberrations. Using checkerboards and a homogeneously illuminated white wall as ideal test patterns, we investigate the influence of lens settings and camera orientation. More precisely, we use images of the `Dresden Image Database' and a specifically created data set to investigate relations between the appearance of optical aberrations and lens settings, focal length, focus, and aperture. Our experiments use images of natural scenes to point to general difficulties that have to be considered when vignetting and radial lens distortion are analyzed in realistic scenarios.
The analysis of lateral chromatic aberration forms another ingredient for a well equipped toolbox of an image
forensic investigator. Previous work proposed its application to forgery detection1 and image source identification.2 This paper takes a closer look on the current state-of-the-art method to analyse lateral chromatic
aberration and presents a new approach to estimate lateral chromatic aberration in a runtime-efficient way. Employing
a set of 11 different camera models including 43 devices, the characteristic of lateral chromatic aberration
is investigated in a large-scale. The reported results point to general difficulties that have to be considered in
real world investigations.
Within this article, we investigate possibilities for identifying the origin of images acquired with flatbed scanners. A current method for the identification of digital cameras takes advantage of image sensor noise, strictly speaking, the spatial noise. Since flatbed scanners and digital cameras use similar technologies, the utilization of image sensor noise for identifying the origin of scanned images seems to be possible.
As characterization of flatbed scanner noise, we considered array reference patterns and sensor line reference patterns. However, there are particularities of flatbed scanners which we expect to influence the identification. This was confirmed by extensive tests: Identification was possible to a certain degree, but less reliable than digital camera identification. In additional tests, we simulated the influence of flatfielding and down scaling as examples for such particularities of flatbed scanners on digital camera identification. One can conclude from the results achieved so far that identifying flatbed scanners is possible. However, since the analyzed methods are not able to determine the image origin in all cases, further investigations are necessary.
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