Selection-Channel-Aware Deep Neural Network to Detect Motion Vector Embedding of HEVC Videos

Huang, Xiongbo; Hu, Yongjian; Wang, Yufei; Liu, Beibei; Liu, Shuowei

doi:10.1109/icspcc50002.2020.9259551

Cited by 3 publications

(8 citation statements)

References 22 publications

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“…e fourth category is steganalysis algorithms [57] designed based on the fact that MVs of subblocks in a macroblock are usually different. In addition, there are also steganalysis methods based on convolutional neural networks [85,86], which is the fifth category.…”

Section: Review Of Mv-based Video Steganalysismentioning

confidence: 99%

“…ey performed experimental validation for the traditional MV steganographic algorithm [38,39] in the HEVC standard and obtained better results. Based on this, Huang et al [86] further introduced the selection-channel-aware mechanism to improve the performance of steganalysis. e literature [85,86] has made useful explorations in deep learning-based steganalysis of MV domains.…”

Section: Steganalysis Based On Convolutional Neural Networkmentioning

confidence: 99%

“…Based on this, Huang et al [86] further introduced the selection-channel-aware mechanism to improve the performance of steganalysis. e literature [85,86] has made useful explorations in deep learning-based steganalysis of MV domains. However, it is still a challenge to propose more effective convolutional neural networks for minimizing embedding distortion in adaptive MV-based steganography.…”

Section: Steganalysis Based On Convolutional Neural Networkmentioning

confidence: 99%

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A Review of Motion Vector-Based Video Steganography

Zhang

Niu

et al. 2022

Security and Communication Networks

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Steganography is a popular research direction in the field of information security. Due to the widespread use of video media, video steganography has received much attention from the research community. Among video steganography, motion vector (MV)-based video steganography has become one of the critical concerns of researchers due to its large embedding capacity and high visual quality. In this article, we focus on the research of MV-based video steganography. Firstly, the basic principles and evaluation criteria for MV-based steganography are discussed. Secondly, according to the different technical characteristics, the MV-based steganography is divided into three categories: the traditional MV domain steganography, the code-based MV domain steganography, and the adaptive MV domain steganography based on the framework of minimizing embedding distortion. The advantages and possible improvement directions of the above representative methods are illustrated. And then, the MV-based video steganalysis is outlined according to different perspectives of feature extraction, which is conducive to the design of better steganography algorithms. Finally, five future research directions are presented, such as designing distortion functions based on multiple factors, embedding methods based on new video coding standards, deep learning-based MV steganography, multidomain embedding strategies, and moving the MV-based steganography from the laboratory into the real world.

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Section: Review Of Mv-based Video Steganalysismentioning

confidence: 99%

Section: Steganalysis Based On Convolutional Neural Networkmentioning

confidence: 99%

Section: Steganalysis Based On Convolutional Neural Networkmentioning

confidence: 99%

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A Review of Motion Vector-Based Video Steganography

Zhang

Niu

et al. 2022

Security and Communication Networks

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“…The first category involves the direct adaptation of classic methods proposed for H.264/AVC to HEVC, including AoSO [23] and NPELO [24] . The second category comprises MV-domain steganalysis methods designed explicitly for HEVC, such as the neural network-based VSRNet method [27] , the candidate list optimality-based LOCL method [30] , and our previous work MVPO [31] . All the steganalysis feature sets are extracted based on the official test model HM16.9.…”

Section: Steganalysis Methodsmentioning

confidence: 99%

“…Shanableh et al [26] extended the idea of the MVC method from H.264/AVC to HEVC. Huang et al [27] introduced convolutional neural networks into MV video steganalysis based on the HEVC standard and proposed the VSRNet (video steganalysis residual network) method. VSRNet constructs independent sub-VSRNet for different embedding capacities and finally connects all subnetworks to form a quantitative steganalysis convolutional neural network capable of estimating capacity.…”

Section: N + 1 Nmentioning

confidence: 99%

High-security HEVC video steganography method using the motion vector prediction index and motion vector difference

Li,

Zhang,

Niu

et al. 2024

Tsinghua Sci. Technol.

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Recently proposed steganalysis methods based on the local optimality of motion vector prediction (MVP) indicate that the existing HEVC (high efficiency video coding) motion vector (MV) domain video steganography algorithms can disturb the optimality of MVP in advanced motion vector prediction (AMVP) technology. In order to improve the security of steganography algorithm, this paper proposes an MV domain steganography method in HEVC based on MVP's index and motion vector difference (MVD). First, we analyze the conditions that need to be met for steganography to resist attacks from MVP's optimality features and other traditional steganalysis features. Then, a distortion function for minimizing embedding distortion is designed, and an algorithm for secret message embedding and extraction in units of inter-frame is proposed.Experimental results show that the proposed algorithm can resist attacks based on the optimality of MVP and also has high security against other traditional steganalysis methods. In addition, the proposed algorithm has excellent performance in visual quality and coding efficiency, and can be applied to practical scenarios of video covert communication.

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HEVC Video Steganalysis Based on PU Maps and Multi-Scale Convolutional Residual Network

Dai,

Wang,

et al. 2024

IEEE Trans. Circuits Syst. Video Technol.

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Selection-Channel-Aware Deep Neural Network to Detect Motion Vector Embedding of HEVC Videos

Cited by 3 publications

References 22 publications

A Review of Motion Vector-Based Video Steganography

A Review of Motion Vector-Based Video Steganography

High-security HEVC video steganography method using the motion vector prediction index and motion vector difference

HEVC Video Steganalysis Based on PU Maps and Multi-Scale Convolutional Residual Network

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