Steganalysis of 3D Objects Using Statistics of Local Feature Sets
This addresses the need for steganalysis in 3D objects, which is an incremental advancement in digital security for graphics and multimedia applications.
The paper tackled the problem of detecting hidden information in 3D graphical objects by using statistical representations of local features like curvature and vertex normals as inputs to machine learning classifiers such as SVM and Fisher Linear Discriminant, achieving performance tested against three watermarking and steganographic methods.
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.