Motion vector-based video steganalysis using spatial-temporal correlation

Ye, Hui; Zhang, Weimin; Yao, Yuanzhi; Kong, Cong; Huang, Hao; Yu, Nenghai

doi:10.1109/cisp.2013.6743975

Cited by 4 publications

(10 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Earlier methods use first order spatial filters but then the following methods integrated second order spatial filters as well. Then, Ye et al [28] showed that using temporal filters contributes to the accuracy which agrees what is shown in Fig. 2.…”

Section: Previous Methodssupporting

confidence: 73%

“…Therefore, in order to design a robust steganalytic system temporal dependency should also be taken into account. There are some methods which considers temporal dependency [9], [15], [19], [20], [28] and some other which does not [8], [16]- [18]. However, none of the previous methods considers temporally neighbouring frames further than one next or previous frames.…”

Section: Previous Methodsmentioning

confidence: 99%

“…Currently, there are limited number of MV steganography [10]- [12], [14], [20]- [27] and MV steganalysis algorithms [8], [9], [13], [15]- [18], [28].…”

Section: Previous Methodsmentioning

confidence: 99%

“…2). Nevertheless, there are only limited number of MV steganalysis methods take temporal dependency into account [9], [13], [15], [28], [29]. 2) Filters in higher orders: The previous methods investigate the correlation between frames which are no further than 2 frames from each other.…”

Section: F Why Strm Is More Accurate and Possible Shortcomings Of Thmentioning

confidence: 99%

See 3 more Smart Citations

Spatio-Temporal Rich Model-Based Video Steganalysis on Cross Sections of Motion Vector Planes

Taşdemir

Kurugöllü

Sezer

2016

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

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.

show abstract

Section: Previous Methodssupporting

confidence: 73%

Section: Previous Methodsmentioning

confidence: 99%

“…Currently, there are limited number of MV steganography [10]- [12], [14], [20]- [27] and MV steganalysis algorithms [8], [9], [13], [15]- [18], [28].…”

Section: Previous Methodsmentioning

confidence: 99%

Section: F Why Strm Is More Accurate and Possible Shortcomings Of Thmentioning

confidence: 99%

See 2 more Smart Citations

Spatio-Temporal Rich Model-Based Video Steganalysis on Cross Sections of Motion Vector Planes

Taşdemir

Kurugöllü

Sezer

2016

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

show abstract

“…In Zhang and XiJian, 6 the method of temporal and spatial correlation based on pixels is applied to steganographic method detection based on MV. Ye et al 7 used four-phase scanning to construct the symbiosis matrix to capture the abnormal changes in the space-time correlation of MVs caused by steganography. Tasdemir et al 8 transferred the SRM (Spatio Rich Model) applied in the field of image steganalysis into the video field, and detected the temporal and spatial characteristic changes of MVs in three dimensions, which has a good effect.…”

Section: Introductionmentioning

confidence: 99%

A video steganalysis method based on coding cost variation

Zhang

et al. 2021

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

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.

show abstract