A video steganalytic algorithm against motion-vector-based steganography

Su, Yuting; Zhang, Chengqian; Zhang, Chuntian

doi:10.1016/j.sigpro.2011.02.012

Cited by 54 publications

(57 citation statements)

References 4 publications

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“…It can be seen that the proposed method significantly outperforms the other three Fig. 9 Comprehensive undetectable performance against steganalysis algorithms S1 [23] and S2 [4] steganographic methods. The Global P E of the proposed method at five embedding rates are all above 0.3.…”

Section: Steganalysis Resultsmentioning

confidence: 91%

“…6 Detection result against S1 [23] and S2 [4] corresponding to different parameter values better depicting the security performance comparision, we illustrate the steganalysis result as follows. Figure 7 shows the detection result against the steganalysis algorithm S1 [23]. S1 extracts a 12-dimension feature from one P-frame utilizing the center of mass (COM) of histogram of motion vector component difference.…”

Section: Steganalysis Resultsmentioning

confidence: 99%

“…When steganalyzers model the horizontal components and the vertical components of one frame as the matrix respectively, most spatial domain steganalysis algorithms in image can be exploited for video steganalysis through combining the temporal correlation. For example, the center of mass (COM) of the histogram characteristic function (HCF) has been used in [23] for motion vector-based video steganalysis. Therefore, we can confirm that the statistical distribution change (SDC) should be considered in designing the distortion function.…”

Section: Statistical Distribution Change Of Motion Vectorsmentioning

confidence: 99%

“…One is Su et al's [23] algorithm (denoted by S1) [4] algorithm (denoted by S2) exploiting the motion vector reversion phenomenon during recompression. As described in these staganalysis algorithms, we use a fixed-size sliding window to scan each subsequence without overlapping, and the steganalytic features representing the clean or stego compressed videos are extracted from the frames within the window.…”

Section: Training and Classificationmentioning

confidence: 99%

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Defining embedding distortion for motion vector-based video steganography

Yao

Zhang

et al. 2014

Multimed Tools Appl

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This paper presents an effective methodology for motion vector-based video steganography. The main principle is to design a suitable distortion function expressing the embedding impact on motion vectors by exploiting the spatial-temporal correlation based on the framework of minimal-distortion steganography. Two factors are considered in the proposed distortion function, which are the statistical distribution change (SDC) of motion vectors in spatial-temporal domain and the prediction error change (PEC) caused by modifying the motion vectors. The practical embedding algorithm is implemented using syndrome-trellis codes (STCs). Experimental results show that the proposed method can enhance the security performance significantly compared with other existing motion vectorbased video steganographic approaches, while obtaining the higher video coding quality as well.

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Section: Steganalysis Resultsmentioning

confidence: 91%

Section: Steganalysis Resultsmentioning

confidence: 99%

Section: Statistical Distribution Change Of Motion Vectorsmentioning

confidence: 99%

Section: Training and Classificationmentioning

confidence: 99%

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Defining embedding distortion for motion vector-based video steganography

Yao

Zhang

et al. 2014

Multimed Tools Appl

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“…This attack is unique in that it takes advantage of a video-specific medium to encode information, meaning that image-based steganographic attacks are inadequate to defeat this type of steganography. Currently, the only observed attacks in literature are passive warden attacks that are specific to motion vector steganography [18], [19].…”

Section: B Video Steganographymentioning

confidence: 99%

A video steganography attack using multi-dimensional Discrete Spring Transform

Sharp

Yang

et al. 2013

2013 IEEE International Conference on Signal and Image Processing Applications

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In this paper, we will further expand upon our proposed Discrete Spring Transform (DST) steganographic attack. We will explore further applications of the transform and how it may be used to defeat multiple steganographic schemes, specifically cur rent video steganography schemes. The effectiveness of the pro posed algorithm will be shown by attacking a multi-dimensional steganographic algorithm embedded in video sequences, where the scheme operates in two different dimensions of the video. The attack is successful in defeating multiple steganographic schemes verified by determining the BER after DST attack which always remains approximately 0.5. Furthermore, the attack preserves the integrity of the video sequence which is verified by determining the PSNR which always remains approximately above 30dB.

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MDE-based image steganography with large embedding capacity

Yin

Luo

2015

Security Comm. Networks

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The big data era calls for image steganography with large embedding capacity and good image quality. Previous methods described in the literature pay more attention to image quality rather than payload. This paper proposes a large capacity steganographic method based on modification direction exploitation and pixel pair matching. By virtue of a reference matrix with particular properties, one or two 9-ary digits can be embedded into each cover pixel pair depending on different payloads. Experimental results demonstrate high embedding capacity as well as good image quality and security. Copyright

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A video steganalytic algorithm against motion-vector-based steganography

Cited by 54 publications

References 4 publications

Defining embedding distortion for motion vector-based video steganography

Defining embedding distortion for motion vector-based video steganography

A video steganography attack using multi-dimensional Discrete Spring Transform

MDE-based image steganography with large embedding capacity

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