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Entropy-based video steganalysis of motion vectors
KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering, Energy and Furnace Technology. KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering, Applied Process Metallurgy.ORCID iD: 0000-0002-5976-2697
2018 (English)In: Entropy, ISSN 1099-4300, E-ISSN 1099-4300, Vol. 20, no 4, article id 244Article in journal (Refereed) Published
Abstract [en]

In this paper, a new method is proposed for motion vector steganalysis using the entropy value and its combination with the features of the optimized motion vector. In this method, the entropy of blocks is calculated to determine their texture and the precision of their motion vectors. Then, by using a fuzzy cluster, the blocks are clustered into the blocks with high and low texture, while the membership function of each block to a high texture class indicates the texture of that block. These membership functions are used to weight the effective features that are extracted by reconstructing the motion estimation equations. Characteristics of the results indicate that the use of entropy and the irregularity of each block increases the precision of the final video classification into cover and stego classes. 

Place, publisher, year, edition, pages
MDPI AG , 2018. Vol. 20, no 4, article id 244
Keywords [en]
Entropy, Motion estimation, Motion vector, Steganalysis, Steganography
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-227372DOI: 10.3390/e20040244ISI: 000435181600033Scopus ID: 2-s2.0-85045835331OAI: oai:DiVA.org:kth-227372DiVA, id: diva2:1212842
Note

Export Date: 9 May 2018; Article; Correspondence Address: Faez, K.; Electrical Engineering Department, Amirkabir University of TechnologyIran; email: kfaez@aut.ac.ir. QC 20180604

Available from: 2018-06-04 Created: 2018-06-04 Last updated: 2018-07-02Bibliographically approved

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Saffari Pour, Mohsen

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