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Proceedings Paper

Identification of image variations based on equivalence classes
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Paper Abstract

This paper presents a fingerprinting method based on equivalence classes. An equivalence class is composed of a reference image and all its variations (or replicas). For each reference image, a decision function is built. The latter determines if a given image belongs to its corresponding equivalence class. This function is built in three steps: synthesis, projection, and analysis. In the first step, the reference image is replicated using different image operators (like JPEG compression, average filtering, etc). During the projection step, the replicas are projected onto a distance space. In the final step, the distance space is analyzed, using machine learning algorithms, and the decision function is built. In this study, three machine learning approaches are compared: orthotope, support vectors machine (SVM), and support vectors data description (SVDD). The orthotope is a computationally efficient ad-hoc method. It consists in building a generalized rectangle in the distance space. The SVM and SVDD are two more general learning algorithms.

Paper Details

Date Published: 31 July 2006
PDF: 12 pages
Proc. SPIE 5960, Visual Communications and Image Processing 2005, 59601R (31 July 2006); doi: 10.1117/12.631578
Show Author Affiliations
Y. Maret, École Polytechnique Fédérale de Lausanne (Switzerland)
G. N. Garcia, École Polytechnique Fédérale de Lausanne (Switzerland)
T. Ebrahimi, École Polytechnique Fédérale de Lausanne (Switzerland)


Published in SPIE Proceedings Vol. 5960:
Visual Communications and Image Processing 2005
Shipeng Li; Fernando Pereira; Heung-Yeung Shum; Andrew G. Tescher, Editor(s)

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