Spatio-temporal rich model-based video steganalysis on cross sections of motion vector planes.

A rich model-based motion vector (MV) steganalysis benefiting from both temporal and spatial correlations of MVs is proposed in this paper. The proposed steganalysis method has a substantially superior detection accuracy than the previous methods, even the targeted ones. The improvement in detection accuracy lies in several novel approaches introduced in this paper. First, it is shown that there is a strong correlation, not only spatially but also temporally, among neighbouring MVs for longer distances. Therefore, temporal MV dependency alongside the spatial dependency is utilized for rigorous MV steganalysis. Second, unlike the filters previously used, which were heuristically designed against a specific MV steganography, a diverse set of many filters, which can capture aberrations introduced by various MV steganography methods is used. The variety and also the number of the filter kernels are substantially more than that of used in the previous ones. Besides that, filters up to fifth order are employed whereas the previous methods use at most second order filters. As a result of these, the proposed system captures various decorrelations in a wide spatio-temporal range and provides a better cover model. The proposed method is tested against the most prominent MV steganalysis and steganography methods. To the best knowledge of the authors, the experiments section has the most comprehensive tests in MV steganalysis field, including five stego and seven steganalysis methods. Test results show that the proposed method yields around 20% detection accuracy increase in low payloads and 5% in higher payloads.

A rich model-based motion vector (MV) steganalysis
benefiting from both temporal and spatial correlations of MVs
is proposed in this paper. The proposed steganalysis method has
a substantially superior detection accuracy than the previous
methods, even the targeted ones. The improvement in detection
accuracy lies in several novel approaches introduced in this
paper. First, it is shown that there is a strong correlation, not
only spatially but also temporally, among neighbouring MVs for
longer distances. Therefore, temporal MV dependency alongside
the spatial dependency is utilized for rigorous MV steganalysis.
Second, unlike the filters previously used, which were heuristically
designed against a specific MV steganography, a diverse
set of many filters, which can capture aberrations introduced
by various MV steganography methods is used. The variety and
also the number of the filter kernels are substantially more than
that of used in the previous ones. Besides that, filters up to fifth
order are employed whereas the previous methods use at most
second order filters. As a result of these, the proposed system
captures various decorrelations in a wide spatio-temporal range
and provides a better cover model. The proposed method is tested
against the most prominent MV steganalysis and steganography
methods. To the best knowledge of the authors, the experiments
section has the most comprehensive tests in MV steganalysis field,
including five stego and seven steganalysis methods. Test results
show that the proposed method yields around 20% detection
accuracy increase in low payloads and 5% in higher payloads.

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