Mr. Gary N. Garcia Molina Profile

Gary N. Garcia Molina

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Proceedings Article | 24 June 2005 Paper

Identification of image variations based on equivalence classes

Y. Maret, G. Garcia, T. Ebrahimi

Proceedings Volume 5960, 59601R (2005) https://doi.org/10.1117/12.631578

KEYWORDS: Image compression, Machine learning, Digital watermarking, Computer programming, Image filtering, Multimedia, System identification, Image analysis, Radon transform, Image enhancement

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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.

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