Deep Learning-based Quantitative Steganalysis to Detect Motion Vector Embedding of HEVC Videos

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

doi:10.1109/dsc50466.2020.00030

Cited by 11 publications

(9 citation statements)

References 18 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%

“…Deep learning-based steganalysis has made significant progress in image steganalysis. Huang et al [85] introduced convolutional neural networks to the quantitative steganalysis of MV video based on the HEVC standard. ey proposed the VSRNet (Video Steganalysis Residual Network) network structure, whose input data contain the MV matrix and the prediction residual matrix.…”

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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“…Huang et al [66] proposed the first deep learning-based video quantitative steganalysis technique. The features are extracted from 4 × 4 PU of each frame since it is the most basic unit in the HEVC video standard.…”

Section: Video Steganalysismentioning

confidence: 99%

Comprehensive Survey of Multimedia Steganalysis: Techniques, Evaluations, and Trends in Future Research

Shehab

Alhaddad

2022

Symmetry

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During recent years, emerging multimedia processing techniques with information security services have received a lot of attention. Among those trends are steganography and steganalysis. Steganography techniques aim to hide the existence of secret messages in an innocent-looking medium, where the medium before and after embedding looks symmetric. Steganalysis techniques aim to breach steganography techniques and detect the presence of invisible messages. In the modern world, digital multimedia such as audio, images, and video became popular and widespread, which makes them perfect candidates for steganography. Monitoring this huge multimedia while the user communicates with the outside world is very important for detecting whether there is a hidden message in any suspicious communication. However, steganalysis has a significant role in many fields, such as to extract the stego-message, to detect suspicious hidden messages and to evaluate the robustness of existing steganography techniques. This survey provides the general principles of hiding secret messages using digital multimedia as well as reviewing the background of steganalysis. In this survey, the steganalysis is classified based on many points of view for better understanding. In addition, it provides a deep review and summarizes recent steganalysis approaches and techniques for audio, images, and video. Finally, the existing shortcomings and future recommendations in this field are discussed to present a useful resource for future research.

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“…Low bitrate videos usually have shorter motion vectors and greater prediction residuals than their high bitrate versions. On the contrary, high bitrate videos usually have smaller prediction residuals than their low bitrate versions [39]. …”

Section: General Steganalysis Rules and Principlesmentioning

confidence: 99%