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Steganography on 3D Models Using a Spatial Subdivision Technique

机译:使用空间细分技术在3D模型上的隐写术

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This paper proposes a new steganography algorithm for 3D models using a spatial subdivision technique. Our algorithm first decomposes the bounding volume of the cover model into voxels based on a Binary Space Partitioning (BSP) tree. The voxels are then further categorized into eight subspaces, each of which is numbered and represented as three-digit binary characters. In the embedding process, we first traverse the BSP tree, locating a leaf voxel; then we embed every three bits of the payload message into the vertex inside the leaf voxel. This is realized by translating a vertex’s current position to the corresponding numbered subspace. This technique is a substitutive blind extraction scheme, where messages embedded can be extracted without the aid of the original cover model. This technique achieves high data capacity, equivalent to at least three times the number of the embedded vertices in the cover model. In addition, the stego model has insignificant visual distortion. Finally, this scheme is robust against similarity transformation attacks.
机译:本文用空间细分技术提出了一种用于3D模型的新的隐写算法。我们的算法首先基于二进制空间分区(BSP)树将封面模型的边界体积分解成体素。然后将体素进一步分为八个子空间,每个子空间都被编号并表示为三位数二进制字符。在嵌入过程中,我们首先遍历BSP树,定位叶体素;然后我们将有效载荷消息的每三位嵌入到叶体素内的顶点。这通过将顶点的当前位置转换为相应的编号子空间来实现这一点。该技术是一种取代盲提取方案,其中可以在没有原始封面模型的帮助下提取嵌入的消息。该技术实现了高数据容量,相当于封面模型中的嵌入顶点的数量的至少三倍。此外,STEGO模型具有微不足道的视觉失真。最后,该方案对抗相似性转换攻击是稳健的。

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