Abstract
This paper introduces a novel method for extracting
sets of feature from 3D objects characterising a robust stegan-
alyzer. Specifically, the proposed steganalyzer should mitigate
the Cover Source Mismatch (CSM) paradigm. A steganalyzer
is considered as a classifier aiming to identify separately cover
and stego objects. A steganalyzer behaves as a classifier by
considering a set of features extracted from cover stego pairs of
3D objects as inputs during the training stage. However, during
the testing stage, the steganalyzer would have to identify whether
specific information was hidden in a set of 3D objects which can
be different from those used during the training. Addressing the
CSM paradigm corresponds to testing the generalization ability
of the steganalyzer when introducing distortions in the cover
objects before hiding information through steganography. Our
method aims to select those 3D features that model best the
changes introduced in objects by steganography or information
hiding and moreover they are able to generalize for different
objects, not present in the training set. The proposed robust
steganalysis approach is tested when considering changes in
3D objects such as those produced by mesh simplification and
additive noise. The results obtained from this study show that
the steganalyzers trained with the selected set of robust features
achieve better detection accuracy of the changes embedded in
the objects, when compared to other sets of features.
sets of feature from 3D objects characterising a robust stegan-
alyzer. Specifically, the proposed steganalyzer should mitigate
the Cover Source Mismatch (CSM) paradigm. A steganalyzer
is considered as a classifier aiming to identify separately cover
and stego objects. A steganalyzer behaves as a classifier by
considering a set of features extracted from cover stego pairs of
3D objects as inputs during the training stage. However, during
the testing stage, the steganalyzer would have to identify whether
specific information was hidden in a set of 3D objects which can
be different from those used during the training. Addressing the
CSM paradigm corresponds to testing the generalization ability
of the steganalyzer when introducing distortions in the cover
objects before hiding information through steganography. Our
method aims to select those 3D features that model best the
changes introduced in objects by steganography or information
hiding and moreover they are able to generalize for different
objects, not present in the training set. The proposed robust
steganalysis approach is tested when considering changes in
3D objects such as those produced by mesh simplification and
additive noise. The results obtained from this study show that
the steganalyzers trained with the selected set of robust features
achieve better detection accuracy of the changes embedded in
the objects, when compared to other sets of features.
| Original language | English |
|---|---|
| Title of host publication | Proc. of International Conference on Pattern Recognition (ICPR) |
| Publisher | IEEE |
| Pages | 4251-4256 |
| Number of pages | 6 |
| Publication status | Published - Dec 2016 |