Abstract
An interesting and challenging problem in digital image forensics is the identification of the device used to acquire an image. Although the source imaging device can be retrieved exploiting the file’s header (e.g., EXIF), this information can be easily tampered. This lead to the necessity of blind techniques to infer the acquisition device, by processing the content of a given image. Recent studies are concentrated on exploiting sensor pattern noise, or extracting a signature from the set of pictures. In this paper we compare two popular algorithms for the blind camera identification. The first approach extracts a fingerprint from a training set of images, by exploiting the camera sensor’s defects. The second one is based on image features extraction and it assumes that images can be affected by color processing and transformations operated by the camera prior to the storage. For the comparison we used two representative dataset of images acquired, using consumer and mobile cameras respectively. Considering both type of cameras this study is useful to understand whether the theories designed for classic consumer cameras maintain their performances on mobile domain.
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Farinella, G.M., Giuffrida, M.V., Digiacomo, V., Battiato, S. (2015). On Blind Source Camera Identification. In: Battiato, S., Blanc-Talon, J., Gallo, G., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2015. Lecture Notes in Computer Science(), vol 9386. Springer, Cham. https://doi.org/10.1007/978-3-319-25903-1_40
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DOI: https://doi.org/10.1007/978-3-319-25903-1_40
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