This thesis proposes three novel solutions to the problem of detection of existence of secret messages embedded in motion vectors (MY) of a video. To this end, behaviours of MVs of natural videos are thoroughly examined in the beginning of the thesis. It has been demonstrated that MVs have strong spatial and correlation and the correlation strength is measured for the first time. Then, the following algorithms are developed considering this fact. Firstly, a novel flatness measure for video steganalysis targeting LSB based motion vector steganography has introduced. Secondly, a spatio-temporal rich model of motion vector planes as a part of a full steganalytic system against motion vector based steganography is proposed. Rich models, which have been used in image steganalysis, were developed to capture the natural correlation among image pixels. This idea is extended to motion vector based steganalysis. Lastly, a rich model based motion vector steganalysis benefiting from both temporal and spatial correlations of motion vectors is presented. The proposed 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 thesis: Firstly, it is shown that there is a strong correlation for longer distances between not only spatially but also temporally neighbouring motion vectors. Therefore, temporal motion vector dependency is utilized along side the spatial dependency. Secondly, unlike the filters previously used, which were heuristically designed against a specific motion vector steganography, a diverse set of many filters which can capture aberrations introduced by various motion vector steganography methods is employed. The variety and also the number of the filter kernels are substantially more than that of previous ones. Also filters up to fifth order are employed whereas at most second order filters are used by previous methods. As a result of these novelties, the proposed system can capture various de-correlations in a wide spatio-temporal range and provide a better cover model.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:676708 |
| Date | January 2015 |
| Creators | Tasdemir, Kasim |
| Publisher | Queen's University Belfast |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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