  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.
