Video Steganalysis Against Motion Vector-Based Steganography by Adding or Subtracting One Motion Vector Value

Wang, Keren; Zhao, Hong; Wang, Hongxia

doi:10.1109/tifs.2014.2308633

Cited by 77 publications

(69 citation statements)

References 18 publications

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“…Although phase modifying stego algorithms evidently cause more degradations in MV patterns than magnitude modifying stego algorithms, they are, however, surprisingly more secure against previous MV steganalysis methods. The reason is that the vast majority of the previous MV steganalysis algorithms design their features by assuming that the magnitudes of message carrier MV components are slightly modified [8], [9], [13], [17], [18], [29]. For example, the most recent MV steganalysis method, AoSO [17], investigates whether the current MV is shrunk or enlarged by 1.…”

Section: Previous Methodsmentioning

confidence: 99%

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

Taşdemir

Kurugöllü

Sezer

2016

IEEE Trans. on Image Process.

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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.

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Section: Previous Methodsmentioning

confidence: 99%

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

Taşdemir

Kurugöllü

Sezer

2016

IEEE Trans. on Image Process.

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“…In [10], researchers introduced method for video steganography based on motion vectors. This work model hides the secret information in the least significant bit of the motion vectors.…”

Section: Literature Reviewmentioning

confidence: 99%

Video Steganography using Zero Order Hold Method for Secured Data Transmission

Shashidhara¹,

Usha²

2017

IJCA

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Steganography used for secure transmission of secret message. Message transmitted over the internet facing malicious attack. So, steganography is needed for secure transmission of secret message. There are two methods which are used for secure transmission. The first method cryptography, secret message which needs to be transmitted over the internet is encrypted using encryption key and sent over the communication channel and at the receiver side only right person with valid key can retrieve the secret message. The second method is steganography, in this method secret message hidden inside the cover media like image, audio, video etc. In this paper, we have presented video steganography using Zero Order Hold(ZOH) technique. Zero Order Hold is basically zooming method, it is used for image zooming. By applying ZOH method, secret information embedded in the cover video.

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“…And two-layered STCs [15] are used to minimize distortion for embedding process. In order to resist the steganalytic schemes based on MV's local optimality [16], several approaches were proposed. In [5], Cao et al exploited the opportunity to optimize the ME perturbation using the loss caused by video compression process.…”

Section: Introductionmentioning

confidence: 99%

“…In Wang et al 's work [17], the calibration-based steganalysis is further improved by matching the parameters between the first and second compression process. Wang et al [16] extracted features based on the difference between the actual the sum of absolute difference (SAD) and locally optimal SAD after the adding-or-subtracting-one operation on MVs. Recently Zhang et al [18] suggested checking the local optimality of MVs by considering both distortion and bit estimation associated with MVs.…”

Section: Introductionmentioning

confidence: 99%

Segmentation Based Video Steganalysis to Detect Motion Vector Modification

Wang

Cao

Zhao

2017

Security and Communication Networks

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This paper presents a steganalytic approach against video steganography which modifies motion vector (MV) in content adaptive manner. Current video steganalytic schemes extract features from fixed-length frames of the whole video and do not take advantage of the content diversity. Consequently, the effectiveness of the steganalytic feature is influenced by video content and the problem of cover source mismatch also affects the steganalytic performance. The goal of this paper is to propose a steganalytic method which can suppress the differences of statistical characteristics caused by video content. The given video is segmented to subsequences according to block's motion in every frame. The steganalytic features extracted from each category of subsequences with close motion intensity are used to build one classifier. The final steganalytic result can be obtained by fusing the results of weighted classifiers. The experimental results have demonstrated that our method can effectively improve the performance of video steganalysis, especially for videos of low bitrate and low embedding ratio.

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Video Steganalysis Against Motion Vector-Based Steganography by Adding or Subtracting One Motion Vector Value

Cited by 77 publications

References 18 publications

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

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

Video Steganography using Zero Order Hold Method for Secured Data Transmission

Segmentation Based Video Steganalysis to Detect Motion Vector Modification

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