BLIND VIDEO STEGANALYSIS BY SEMI-SUPERVISED APPROACH FOR MOTION VECTORS BASED STEGANOGRAPHY ALGORITHMS

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MORTAZAVI MEHRIZI J.; KHADEMI M.; SADOGHI YAZDI H.

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Paper Information

Journal: NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR Year:2015 | Volume:13 | Issue:2 Page(s): 89-100

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Information Journal Paper

Title

BLIND VIDEO STEGANALYSIS BY SEMI-SUPERVISED APPROACH FOR MOTION VECTORS BASED STEGANOGRAPHY ALGORITHMS

Author(s)

MORTAZAVI MEHRIZI J. | KHADEMI M. | SADOGHI YAZDI H. | Issue Writer Certificate

Keywords

SEMI-SUPERVISED GROWING SELF-ORGANIZING MAPSQ2

SEMI-SUPERVISED LEARNINGQ2

VIDEO BLIND STEGANALYSISQ2

VVIDEO STEGANOGRAPHYQ2

Abstract

Supervised learning algorithms are widely used in blind video steganalysis and the cost of generating labeled data in them is high. That is why only a limited number of steganography algorithms with accessible code can be used for the training the classifier. Therefore, we cannot be sure about the effectiveness of steganalyzer in identifying non-accessible video steganography algorithms. On the other hand, using offline classification methods in the blind video steganalysis causes the learning process be time consuming and the system cannot be updated online. To solve this problem, we propose a new method for the blind video steganalysis by SEMI-SUPERVISED LEARNING approach. In the proposed method, by eliminating the limitation of labeled training dataset, the classifier performance is improved for video steganography algorithms with non-accessible code. It is also proved that the proposed method, compared to common classification methods for the blind video steganalysis, has less time complexity and it is an optimal online technique. The simulation results on the standard database show that in addition to the above advantages, this method has appropriate accuracy and is comparable to common methods.

Cites

GEOMETRICAL SELF-ORGANIZING MAP CLASSIFIER BASED ON ACTIVE LEARNING FOR STEGANALYSIS IN THE VIDEO ENVIRONMENT BY SPENDING AT LEAST A LABEL

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APA: Copy

MORTAZAVI MEHRIZI, J., KHADEMI, M., & SADOGHI YAZDI, H.. (2015). BLIND VIDEO STEGANALYSIS BY SEMI-SUPERVISED APPROACH FOR MOTION VECTORS BASED STEGANOGRAPHY ALGORITHMS. NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR, 13(2), 89-100. SID. https://sid.ir/paper/228543/en

Vancouver: Copy

MORTAZAVI MEHRIZI J., KHADEMI M., SADOGHI YAZDI H.. BLIND VIDEO STEGANALYSIS BY SEMI-SUPERVISED APPROACH FOR MOTION VECTORS BASED STEGANOGRAPHY ALGORITHMS. NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR[Internet]. 2015;13(2):89-100. Available from: https://sid.ir/paper/228543/en

IEEE: Copy

J. MORTAZAVI MEHRIZI, M. KHADEMI, and H. SADOGHI YAZDI, “BLIND VIDEO STEGANALYSIS BY SEMI-SUPERVISED APPROACH FOR MOTION VECTORS BASED STEGANOGRAPHY ALGORITHMS,” NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR, vol. 13, no. 2, pp. 89–100, 2015, [Online]. Available: https://sid.ir/paper/228543/en

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