Video Steganography Techniques in Spatial Domain—A Survey

Dalal, Mukesh; Juneja, Mamta

doi:10.1007/978-981-10-6890-4_67

Cited by 28 publications

(7 citation statements)

References 14 publications

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“…Zhang et al [33] mainly summarized video steganalysis's research status and development direction for different embedding domains. Dalal et al [34] reviewed the video steganography based on the spatial domain. Liu et al [35] classified video steganography into intra-embedding, preembedding, and postembedding.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

A Review of Motion Vector-Based Video Steganography

Zhang

Niu

et al. 2022

Security and Communication Networks

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“…The effectiveness of the system evaluated using 10,000 speech audio clips and 10,000 music in which coding based audio steganography ensures effective results. Dalal M., et al, surveying the video steganography techniques presented in the spatial domain [34]. This process examines the various spatial domain steganography techniques to manage steganography parameters such as robustness, imperceptibility, and capacity.…”

Section: Related Workmentioning

confidence: 99%

Adopt an optimal location using a genetic algorithm for audio steganography

Raheema¹

2021

PEN

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With the development of technologies, most of the users utilizing the Internet for transmitting information from one place to another place. The transmitted data may be affected because of the intermediate user. Therefore, the steganography approach is applied for managing the secret information. Here audio steganography is utilized to maintain the secret information by hiding the image into the audio files. In this work, discrete cosine transforms, and discrete wavelet transform is applied to perform the Steganalysis process. The optimal hiding location has been identified by using the optimization technique called a genetic algorithm. The method utilizes the selection, crossover and mutation operators for selecting the best location. The chosen locations are difficult to predict by unauthorized users because the embedded location is varied from information to information. Then the efficiency of the system ensures the high PSNR, structural similarity index (SSIM), minimum mean square error value and Jaccard, which is evaluated on the audio Steganalysis dataset.

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“…Video steganography methods can be divided into two main categories: inter-frame and intra-frame steganography methods. Intra-frame methods manipulate each video frame individually, and regardless of dependencies among frames [12,22,23,31,36,56]. Except for capacity, intra-frame methods have no better performance than image steganography methods in the case of steganography criteria.…”

Section: Related Work On Video Steganography and Steganalysismentioning

confidence: 99%

Blind MV-based video steganalysis based on joint inter-frame and intra-frame statistics

Ghamsarian

Schoeffmann

Khademi

2020

Multimed Tools Appl

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Despite all its irrefutable benefits, the development of steganography methods has sparked ever-increasing concerns over steganography abuse in recent decades. To prevent the inimical usage of steganography, steganalysis approaches have been introduced. Since motion vector manipulation leads to random and indirect changes in the statistics of videos, MV-based video steganography has been the center of attention in recent years. In this paper, we propose a 54-dimentional feature set exploiting spatio-temporal features of motion vectors to blindly detect MV-based stego videos. The idea behind the proposed features originates from two facts. First, there are strong dependencies among neighboring MVs due to utilizing rate-distortion optimization techniques and belonging to the same rigid object or static background. Accordingly, MV manipulation can leave important clues on the differences between each MV and the MVs belonging to the neighboring blocks. Second, a majority of MVs in original videos are locally optimal after decoding concerning the Lagrangian multiplier, notwithstanding the information loss during compression. Motion vector alteration during information embedding can affect these statistics that can be utilized for steganalysis. Experimental results have shown that our features’ performance far exceeds that of state-of-the-art steganalysis methods. This outstanding performance lies in the utilization of complementary spatio-temporal statistics affected by MV manipulation as well as feature dimensionality reduction applied to prevent overfitting. Moreover, unlike other existing MV-based steganalysis methods, our proposed features can be adjusted to various settings of the state-of-the-art video codec standards such as sub-pixel motion estimation and variable-block-size motion estimation.

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Video Steganography Techniques in Spatial Domain—A Survey

Cited by 28 publications

References 14 publications

A Review of Motion Vector-Based Video Steganography

A Review of Motion Vector-Based Video Steganography

Adopt an optimal location using a genetic algorithm for audio steganography

Blind MV-based video steganalysis based on joint inter-frame and intra-frame statistics

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