Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/37625
標題: A NOVEL HIGH CAPACITY 3D STEGANOGRAPHIC ALGORITHM
作者: Li, Meng-Tsan
Huang, Nien-Ching
Wang, Chung-Ming
關鍵字: 3D steganography
Polygon model
Data embedding
High capacity
point-sampled geometry
polygonal meshes
hiding algorithm
摘要: This paper presents a powerful high capacity 3D steganographic algorithm which can embed more than 9.6 million bits of a secret message using a 3D polygon model with only 14,004 vertices. To the best of our knowledge, this is the largest capacity ever reported in the literature. We accomplish this novel algorithm by using two levels of message encoding. In the first level, we subdivide the secret message into a number of payload fragments. We then encode each payload fragment by utilizing a corresponding random sample generated on a unit sphere. In the second level, we encode these random samples to a number of feature points which convey the payload fragment and are skillfully located on the surface of an existing 3D polygon model. The procedure produces a point cloud stego model that can be delivered in a public channel for steganographic purposes. We produce dummy points by uniform sampling over the surface area of the 3D polygon model which increases the rendering quality of the point cloud stego model and also prevents suspicion of the messages by eavesdroppers. Our scheme has five features. First, it can embed a significantly large number of payloads using 3D polygonal models with small complexity. Theoretical analysis indicates that our algorithm has a significantly high capacity with the magnitude of 50 bits per feature point, outperforming that offered by the current state-of-the-art 3D steganographic algorithms. Second, the scheme is flexible, allowing it to convey various capacities by adjusting two region partition parameters. Third, it belongs to a distortion-free manner, and encounters no model variation because of the hidden message. Fourth, the algorithm belongs to a blind extraction, enabling it to extract the secret message without referring to the original cover model. Finally, the scheme provides independency for the secret payload embedding and supports progressive message extraction.
URI: http://hdl.handle.net/11455/37625
ISSN: 1349-4198
Appears in Collections:資訊科學與工程學系所

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