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標題: 轉換域的三維模型資訊偽裝演算法之研究
A Steganographic Algorithm for 3D Polygonal Meshes in the Transform Domain
作者: 張聖杰 
Chang, Sheng-Chieh 
關鍵字: Polygonal Meshes;多面體網格;Steganography;Transform Domain;資訊偽裝;轉換域
出版社: 資訊科學系所
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有鑑於此,本篇論文提出一個新的、基於轉換域、具盲擷取性的三維資訊偽裝演算法,可大幅提高資訊嵌入容量。首先,我們輸入位處世界座標空間內的多面體模型。我們利用頂點資訊求出頂點向量,並將之先轉換至特徵空間,最後轉換至球面座標系統。接著,我們給定量化刻度參數,並透過量化刻度調變(Quantization Index Modulation, QIM)的技巧,在嵌入0、1字串訊息時改變三維模型頂點向量的長度及方向。最後,我們將偽裝模型逆向轉換回原始的世界座標空間,即可建構出偽裝模型。擷取訊息則將偽裝模型依記錄的量化刻度參數以相同方式行之。此外,為了能提高可用模型之複雜度,我們更提出一個改良的演算法。實驗結果顯示:我們的演算法可嵌入接近於模型頂點個數三倍的秘密訊息。演算法僅需原始量化刻度參數,不需原始模型即可正確地擷取出秘密訊息。適用的模型複雜度最多可達13萬個頂點。此外,掩護模型與偽裝模型間之變形以肉眼觀察難以察覺。差異程度經量化後,RMSE在0.001%至0.7%之間,Hausdorff距離平均值在0.001%至0.6%之間。最後,我們的演算法可有效抵抗相似轉換(affine transformation)攻擊,包含平移、旋轉及等向縮放等。

The steganography for 3D models investigates techniques for embedding the secret message into 3D models for the converted communications. Algorithms for 3D steganography can be classified into the spatial domain and the transform domain, according to the approach they embed the secret message. Based on the transform domain, the distortion is dispersed globally on the 3D stego model, reducing potential abrupt visual aliasing. However, current algorithms in the transform domain suffer from having low embedding capacity, which severely hampers the scope of steganographic applications.
This thesis proposes a new, blind detection, high capacity steganographic algorithm for 3D polygonal meshes in the transform domain. First, given a 3D polygonal mesh in the world coordinate space, our algorithm determines the vertex vectors based on the vertex coordinate information. Second, we transform the vertex vectors to the feature space and then to the spherical coordinate space. Third, given a quantity parameter, we employ the skill of Quantization Index Modulation (QIM) to change the length as well as the direction of the vertex vectors for embedding the secret message containing the 0 and 1 bits string. Finally, we re-convert the stego model from the current spherical coordinate space to the world coordinate space. Extracting the message from the 3D model is proceeded on the reverse approach of the embedding steps using the same quantity parameter. In addition, we propose an improved algorithm, allowing our algorithm to support complex models with up to 130000 vertices.
Experimental results show that the message capacity of our algorithm is nearly three times the numbers of the vertices in the 3D model. We can extract the secret message requiring no aid of the cover model, achieving the blind detection mechanism. Furthermore, the stego model shows insignificant visual difference, in comparison to the cover model. This also demonstrates the imperceptibility features of our algorithm. The root mean square errors between two models range from 0.001% to 0.7%, while the mean of the Hausdorff distance ranges from 0.001% to 0.6%. Finally, our algorithm is robust against affine transformation attacks, including translation, rotation and uniform scaling.
In conclusion, our algorithm has high message capacity, which is superior to our counterparts in the transform domain. The algorithm is with blind detection feature, and supports rather complex models. The message embedding does not introduce significant visual distortion to the stego model. Finally, our algorithm is robust against affine transformation attacks. The algorithm we propose is feasible to the applications of the steganography.
其他識別: U0005-1108200620494300
Appears in Collections:資訊科學與工程學系所

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