A video steganalysis method based on coding cost variation
Aiming at the problems existing in existing steganalysis algorithms, this article proposes Motion Vector Coding Cost Change video steganalysis features based on Improved Motion Vector Reversion-Based features and Subtractive Probability of Coding Cost Optimal Matching features based on Subtractive P...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-02-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147721992730 |
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| _version_ | 1832547914131439616 |
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| author | Jianyi Liu Cong Zhang Ru Zhang Yi Li Jie Cheng |
| author_facet | Jianyi Liu Cong Zhang Ru Zhang Yi Li Jie Cheng |
| author_sort | Jianyi Liu |
| collection | DOAJ |
| description | Aiming at the problems existing in existing steganalysis algorithms, this article proposes Motion Vector Coding Cost Change video steganalysis features based on Improved Motion Vector Reversion-Based features and Subtractive Probability of Coding Cost Optimal Matching features based on Subtractive Probability of Optimal Matching features from the perspective of the change of coding cost. Motion Vector Coding Cost Change features can be well consistent with the coding cost before recoding by analyzing the sub-pixel coding cost of recoding. By counting the sub-pixel coding costs of motion vectors before and after video recoding, the Sum of Absolute Difference values of motion vectors instead of predicted residuals are applied to steganalysis and detection, and the steganographic algorithm based on motion vectors is effectively detected. Experiments show that Motion Vector Coding Cost Change features have higher detection accuracy than Add-or-Subtract-One, Improved Motion Vector Reversion-Based, and other typical features in various steganography methods, and Subtractive Probability of Coding Cost Optimal Matching features have higher detection effect and better robustness than Subtractive Probability of Optimal Matching features. |
| format | Article |
| id | doaj-art-5279da7f6a934865847c620711e0ea83 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2021-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-5279da7f6a934865847c620711e0ea832025-02-03T06:43:00ZengWileyInternational Journal of Distributed Sensor Networks1550-14772021-02-011710.1177/1550147721992730A video steganalysis method based on coding cost variationJianyi Liu0Cong Zhang1Ru Zhang2Yi Li3Jie Cheng4Beijing University of Posts and Telecommunications, Beijing, ChinaBeijing University of Posts and Telecommunications, Beijing, ChinaBeijing University of Posts and Telecommunications, Beijing, ChinaBeijing University of Posts and Telecommunications, Beijing, ChinaState Grid Information and Telecommunication Branch, Beijing, ChinaAiming at the problems existing in existing steganalysis algorithms, this article proposes Motion Vector Coding Cost Change video steganalysis features based on Improved Motion Vector Reversion-Based features and Subtractive Probability of Coding Cost Optimal Matching features based on Subtractive Probability of Optimal Matching features from the perspective of the change of coding cost. Motion Vector Coding Cost Change features can be well consistent with the coding cost before recoding by analyzing the sub-pixel coding cost of recoding. By counting the sub-pixel coding costs of motion vectors before and after video recoding, the Sum of Absolute Difference values of motion vectors instead of predicted residuals are applied to steganalysis and detection, and the steganographic algorithm based on motion vectors is effectively detected. Experiments show that Motion Vector Coding Cost Change features have higher detection accuracy than Add-or-Subtract-One, Improved Motion Vector Reversion-Based, and other typical features in various steganography methods, and Subtractive Probability of Coding Cost Optimal Matching features have higher detection effect and better robustness than Subtractive Probability of Optimal Matching features.https://doi.org/10.1177/1550147721992730 |
| spellingShingle | Jianyi Liu Cong Zhang Ru Zhang Yi Li Jie Cheng A video steganalysis method based on coding cost variation International Journal of Distributed Sensor Networks |
| title | A video steganalysis method based on coding cost variation |
| title_full | A video steganalysis method based on coding cost variation |
| title_fullStr | A video steganalysis method based on coding cost variation |
| title_full_unstemmed | A video steganalysis method based on coding cost variation |
| title_short | A video steganalysis method based on coding cost variation |
| title_sort | video steganalysis method based on coding cost variation |
| url | https://doi.org/10.1177/1550147721992730 |
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