Steganography through perspective invariance

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Tezin Türü: Doktora

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü, Türkiye

Tezin Onay Tarihi: 2012

Öğrenci: YAĞIZ YAŞAROĞLU

Danışman: ABDULLAH AYDIN ALATAN

Özet:

A novel approach for watermarking of 3D models is introduced, for which data is embedded into 3D models, whereas extracted from their projected 2D visual or 2D-plus-depth representations. Such a watermarking system is valuable, since most of the 3D content is being consumed as 2D visual data. Apart from the efficiency of embedding data into 3D models before generation of arbitrary 2D projections, in some use cases, such as free viewpoint video or computer games, 2D content has to be rendered at the client, where watermarking is less secure. In order to achieve this aim, 3D-2D perspective projection invariants, as well as 3D projective invariants are used and utilization of such invariants enables the method to be independent of the viewpoint from which 2D representations are generated. The first method proposed employs a perspective projection invariant to extract hidden data from an arbitrary 2D view of a watermarked 3D model. Data is encoded in the relative positions of six interest points, selection of which requires minimal criteria. Two main problems for such a watermarking system are identified as noise sensitivity of the invariant and repeatability of the interest point detection. By optimizing an objective function considering this sensitivity, the optimal 3D interest point displacements are obtained. Performance of the proposed system is evaluated through simulations on polygonal 3D mesh models and the results strongly indicate that perspective invariant-based watermarking is feasible. As an extenstion for 2D plus depth representation of 3D models, data embedded in 3D models is also detected by combining information in 2D views and range data by utilizing another projective invariant. Finally, the problem of repeatable interest point detection that remain detectable after data embedding, is also examined and a novel method to identify such repeatable interest points is presented. The proposed methods indicate a new direction in watermarking research.