基于局部特征集统计的三维物体隐写分析
Steganalysis of 3D Objects Using Statistics of Local Feature Sets
摘要：
3D隐写分析旨在通过数字水印或隐写术来识别图形对象中产生的细微的不可见变化。从覆盖和隐写3D网格对象中提取的3D特征的统计表示集被用作机器学习分类器的输入，以决定是否有任何信息隐藏在给定的图形对象中。根据先前的研究，可以使用局部几何特征集来定义隐写和覆盖对象之间的差异。在本文中提出的功能，包括那些代表当地的对象曲率，顶点法线，在球坐标系中的局部几何表示，并被认为是在各种组合与他人。我们还分析了各种3D特征集的有效性应用于隐写分析的基础上的皮尔逊相关系数。本研究所提出的辨识立体隐藏与遮蔽物件的分类器包括支持向量机与Fisher线性判别组合。三种不同的水印和隐写方法用于隐藏信息的3D对象用于测试所提出的隐写分析方法的性能。
英文摘要：
3D steganalysis aims to identify subtle invisible changes produced in graphical objects through digital watermarking or steganography. Sets of statistical representations of 3D features, extracted from both cover and stego 3D mesh objects, are used as inputs into machine learning classifiers in order to decide whether any information was hidden in the given graphical object. According to previous studies, sets of local geometry features can be used to define the differences between stego and cover-objects. The features proposed in this paper include those representing the local object curvature, vertex normals, the local geometry representation in the spherical coordinate system and are considered in various combinations with others. We also analyze the effectiveness of various 3D feature sets applied for steganalysis based on the Pearson correlation coefficient. The classifiers proposed in this study for discriminating the 3D stego and cover-objects include Support Vector Machine and the Fisher Linear Discriminant ensemble. Three different watermarking and steganographic methods are used for hiding information in the 3D objects used for testing the performance of the proposed steganalysis methodology.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
