Study and investigation of video steganography over uncompressed and compressed domain: a comprehensive review

Patel, Rachna; Lad, Kalpesh; Patel, Mukesh

doi:10.1007/s00530-021-00763-z

Cited by 22 publications

(10 citation statements)

References 89 publications

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“…e experimental analysis of some prominent techniques using different quality metrics has also been performed. Patel et al [37] provide a systematic overview of video steganography in the compressed and uncompressed domains. However, there is no published literature specifically summarizing MV-based video steganography techniques to the best of our knowledge.…”

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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“…Various steganography methods and appropriate steganalysis schemes that have a high level of applicability in the field of digital forensics have been presented in work stated by the authors Dalal et al [12], and the same authors [13] have compiled a survey for the efficient implementation of video steganography. The researchers Patel et al [14] presented a full review of the inquiry of video steganography in the uncompressed and compressed domains, in which several compressed and uncompressed mediums with their advantages and disadvantages in terms of information concealing were presented. The authors Lin et al [15] demonstrated feature-based steganalysis for Pixel-Value Differencing Steganography, and the model demonstrates the significance of statistical features-oriented embodiment and detection techniques.…”

Section: Literature Surveymentioning

confidence: 99%

An Improved Steganographic Scheme Using the Contour Principle to Ensure the Privacy of Medical Data on Digital Images

Krishnan¹,

Yuvaraj²,

Devi³

et al. 2023

Computer Systems Science and Engineering

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With the improvement of current online communication schemes, it is now possible to successfully distribute and transport secured digital Content via the communication channel at a faster transmission rate. Traditional steganography and cryptography concepts are used to achieve the goal of concealing secret Content on a media and encrypting it before transmission. Both of the techniques mentioned above aid in the confidentiality of feature content. The proposed approach concerns secret content embodiment in selected pixels on digital image layers such as Red, Green, and Blue. The private Content originated from a medical client and was forwarded to a medical practitioner on the server end through the internet. The K-Means clustering principle uses the contouring approach to frame the pixel clusters on the image layers. The content embodiment procedure is performed on the selected pixel groups of all layers of the image using the Least Significant Bit (LSB) substitution technique to build the secret Content embedded image known as the stego image, which is subsequently transmitted across the internet medium to the server end. The experimental results are computed using the inputs from "Open-Access Medical Image Repositories (aylward.org)" and demonstrate the scheme's impudence as the Content concealing procedure progresses.

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“…The work presented by Dalal and Juneja 24 offered a study about the available schemes of steganography for hiding the secret content without a huge change in the visual quality of the image and the steganalysis process for detecting the availability of secret content in the stego images. Another study was presented by Patel et al., 25 in which a detailed investigation of video steganography over uncompressed and compressed domains with various formats of cover images was presented along with its lossless secret content extraction procedures. An approach of secret stream embedding on color images based on the visual quality of the color value was presented by Abdel Raouf, 26 in which the content embodiment and extraction were well presented with its novelty for the attainment of content extraction in a lossless fashion.…”

Section: Related Workmentioning

confidence: 99%

Approach to secret content tagging on color images with a Sudoku-based scrambling principle

et al. 2023

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At present, there has been much focus on information hiding because it acts as an alternative to secure communication. Secret content is embodied in an image using various image embodiment techniques, with the least significant bit (LSB) substitution being one of the preferred strategies. Asserting the originality and quality of the content embodiment is equally challenging. A scrambling mechanism using a Sudoku scheme is applied on all red, green, and blue layer blocks of the image. The image layers are devised into blocks, and over the blocks unique scrambling procedures are applied to acquire the scrambled image. The scrambled image is then exposed to the confidential stream embodiment using the traditional LSB substitution scheme by following the chess game-based queen tour traversal pattern for locating the pixels. After the embedding process, the scrambled image is fragmented into blocks, and the exact descrambling of the blocks is performed to get back the image, which visually resembles the original image called the stego image. The stego image gets split into blocks at the receiver end to apply exact scrambling on the corresponding blocks, and using the queen tour pattern, the secret stream is to be extracted. As a result of the experiments, it is found that the projected approach is unlikely to be very predictable and unique in its patterns.

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Study and investigation of video steganography over uncompressed and compressed domain: a comprehensive review

Cited by 22 publications

References 89 publications

A Review of Motion Vector-Based Video Steganography

A Review of Motion Vector-Based Video Steganography

An Improved Steganographic Scheme Using the Contour Principle to Ensure the Privacy of Medical Data on Digital Images

Approach to secret content tagging on color images with a Sudoku-based scrambling principle

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