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.
The application of Low-Density Generator Matrix (LDGM) Codes combined with Survey Propagation (SP) in steganography seems to be advantageous, since it is possible to approximate the coset leader directly. Thus, large codeword length can be applied, resulting in an embedding efficiency close to the theoretical upper bound of embedding efficiency. Since this approach is still quite complex, this paper deals with the application of Belief Propagation (BP) to LDGM Codes used to reduce the complexity of embedding while keeping the embedding efficiency constant.
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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