Investigation on principles for cost assignment in motion vector-based video steganography

Li, Jun; Zhang, Minqing; Niu, Ke; Yang, Xiaoyuan

doi:10.1016/j.jisa.2023.103439

Cited by 6 publications

(9 citation statements)

References 25 publications

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“…As can be seen from the data in the figure, the highest detection performance of the proposed feature is achieved when the recompression QP is the same as the QP of the first compression (QP = 25). Although the detection performance of the proposed feature decreases when the recompression QP (22,28) is not the same as the QP of the first compression, the decrease is slight. This is because the proposed feature in this paper mainly consists of the MVP revision feature and the partition state transfer feature of the P-Skip macroblock.…”

Section: Steganalysis Performance In the Case Of Mismatch With Qp Rec...mentioning

confidence: 92%

“…Only those macroblocks' diff that before and after calibration are all P-Skip macroblocks are counted. The stego video is embedded using the PCAMV algorithm [22] with an embedding capacity of 100 bpf. The horizontal coordinate in Figure 2 is the value of diff, and the vertical coordinate is the empirical probability of the occurrence in all P-Skip mac-roblocks.…”

Section: 11mentioning

confidence: 99%

“…To illustrate the steganography's effect on the P‐Skip macroblocks, we embed the message into the foreman.264 standard sequence using an MV‐based steganography algorithm named PCAMV (principles on cost assignment for motion vector) [22]. Table 2 shows the modified number of MVPs and their proportions in the first P‐frame with different QPs and embedding capacities (bits per frame, bpf).…”

Section: Motivationmentioning

confidence: 99%

“…The basic idea is to minimize the overall distortion by designing a distortion function for covers and then combining it with coding methods such as STC [16], which greatly improves the security of steganography algorithms. According to the design perspective of distortion function, the third stage of video steganography can be subdivided into complexity-based methods [17], local optimality-based methods [18][19][20], and multiple factors-based methods [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

“…Detector accuracy (%) of sub-feature component against Tar3[22] with different embedding capacities and QPs using database DB1.…”

mentioning

confidence: 99%

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Motion vector‐domain video steganalysis exploiting skipped macroblocks

Li,

Zhang,

Niu

et al. 2023

IET Image Processing

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Video steganography has the potential to be used to convey illegal information, and video steganalysis is a vital tool to detect the presence of this illicit act. Currently, all the motion vector (MV)‐based video steganalysis algorithms extract feature sets directly from the MVs, but ignoring the embedding operation may perturb the statistical distribution of other video encoding elements, such as the skipped macroblocks (no direct MVs). This paper proposes a novel 11‐dimensional feature set to detect MV‐based video steganography based on the above observation. The proposed feature is extracted based on the skipped macroblocks by recompression calibration. Specifically, the feature consists of two components. The first is the probability distribution of motion vector prediction (MVP) difference, and the second is the probability distribution of partition state transfer. Extensive experiments on different conditions demonstrate that the proposed feature set achieves good detection accuracy, especially in lower embedding capacities. In addition, the loss of detection performance caused by recompression calibration using mismatched quantization parameters (QP) is within the acceptable range, so the proposed method can be used in practical scenarios.

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Section: Steganalysis Performance In the Case Of Mismatch With Qp Rec...mentioning

confidence: 92%

Section: 11mentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Detector accuracy (%) of sub-feature component against Tar3[22] with different embedding capacities and QPs using database DB1.…”

mentioning

confidence: 99%

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Motion vector‐domain video steganalysis exploiting skipped macroblocks

Li,

Zhang,

Niu

et al. 2023

IET Image Processing

Self Cite

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AI‐generated video steganography based on semantic segmentation

Lin,

Luo,

Zhang

et al. 2024

IET Image Processing

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Traditional video steganography methods primarily rely on modifying concealed spaces for embedding, thereby exhibiting a certain degree of security and embedding capacity. Nevertheless, these methods do not fully capitalize on the rich semantic information inherent in videos, limiting their overall effectiveness. In this paper, an AI‐generated video steganography scheme based on semantic segmentation is proposed. The mapping relationship between secret and semantic information is established by using a semantic segmentation model. The secret information can be converted into semantic labels by semantic histograms or pixels means, and semantic labels containing secret information are obtained and input into the video‐to‐video model to drive the generation of stego videos. After receiving the stego video, the receiver extracts the secret information using a pre‐defined specific embedding mode, including the methods of sub‐block partitioning and embedding capacity per frame. The experimental results show that the stego video has good visual quality, security, and robustness against various noise attacks.

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A Comprehensive Review on Steganography Techniques for Text, Images, and Audio

Padmaja,

Basha

2023

2023 IEEE Fifth International Conference on Advances in Electronics, Computers and Communications (ICAECC)

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Investigation on principles for cost assignment in motion vector-based video steganography

Cited by 6 publications

References 25 publications

Motion vector‐domain video steganalysis exploiting skipped macroblocks

Motion vector‐domain video steganalysis exploiting skipped macroblocks

AI‐generated video steganography based on semantic segmentation

A Comprehensive Review on Steganography Techniques for Text, Images, and Audio

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