Segmentation Based Video Steganalysis to Detect Motion Vector Modification

Wang, Peipei; Cao, Yun; Zhao, Xianfeng

doi:10.1155/2017/8051389

Cited by 7 publications

(7 citation statements)

References 24 publications

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“…It is worth noting that the algorithm does not design a new distortion function but improves security through a capacity adjustment strategy, which is theoretically applicable to all distortion functions. However, the algorithm essentially concentrates the embedding capacity on specific frames, which will have security crises if the attacker adopts an adaptive steganalysis strategy [74,75].…”

Section: Other Methods For Designing Distortion Functionmentioning

confidence: 99%

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: Other Methods For Designing Distortion Functionmentioning

confidence: 99%

A Review of Motion Vector-Based Video Steganography

Zhang

Niu

et al. 2022

Security and Communication Networks

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“…Wang et al [60] proposed a steganalytical technique based on motion vectors, taking the advantages of content variety. The video is divided into subclasses; each class contains frames with similar intensity.…”

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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“…Their approach checks the local optimality of motion vectors in a rate-distortion sense. In 2017, Wang et al [69] divided the video into detection intervals (DI) with fixed-length and then extracted the NPELO features [70] from every DI. Sadat et al [55] proposed a motion vectorbased method that extracts intrinsic and statistical features obtained by local optimization of the cost function.…”

Section: Spatialmentioning

confidence: 99%

“…Deep CNN SRM [108]and Bayar [109] Uncompressed GAN-based approach [110] 2021 Yun Cao et al [111] Prediction error domain Ensemble v2.0 [85] COMMM [56] and VDCTR [69].…”

Section: Spatial and Temporalmentioning

confidence: 99%

“…Even if respectable efforts have been made to develop interesting datasets for video steganalysis, it is recurrent in the recent literature that more work is still to be done in this topic. As a big share of the videos in different media are low quality, some authors like Wang, in [69], consider developing solutions for low-quality video especially that these solutions will need only a limited and light computation. As the H.265 (HEVC) video encoding format is making good progress on the Internet and other means, it starts attracting the attention of steganographers.…”

Section: Research Directions That Appear In the Recent Investigation ...mentioning

confidence: 99%

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