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Video steganography approach enables hiding chunks of secret information inside video sequences. The features of video sequences including high capacity as well as complex structure make them more preferable for choosing as cover media over other media such as image, text, or audio. Video steganography is a prominent as well as the evolving field i...
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... Steganography has recently grown in importance as a result of the increasing prevalence of digital media and the necessity to protect sensitive information. Steganography is the process of hiding data inside a carrier like an image, audio, or video file [5][6][7]. It also includes obscuring data inside digital media, instead of protecting it in its original form. ...
... This section presents the process involved in encryption using the 2D-improved henon map. The 2D-improved henon map is based on discrete chaotic system which is mathematically presented in Eq. (6). ...
Steganography is the process of hiding secret data within the image, audio or the text files. Video steganography is a technique where the secret data is hidden within video files which allows for covert communication without any suspicious actions. The existing approaches in steganography face issues related to poor retrieval rate due to poor encryption and decryption. So, this research introduces a hybrid approach which is the combination of 2D-improved Henon map and 3D-logistic map. The proposed method encrypts the secret data by utilizing the hybrid improved chaotic map and contains a de-embedding process for retrieving the secret data at the receiver’s end. Also, the cover video frames are chosen using the AlexNet model depending on the score value of frames. The discrete cosine transform (DCT) block wise embedding is used to embed the secret data in the selected frames. The evaluation involves different cover and secret data pairs including video-image, video-audio and image-image. The PSNR for cover-secret data pairs of video-image and video-audio are measured at 47.05 dB and 41.28 dB. Furthermore, the efficiency of hybrid approach is evaluated against the existing techniques based on image-image steganography. In image-image steganography, the PSNR of color pepper image for Hybrid approach is 51.05 dB, which is higher than the existing Permutation Substitution and Boolean Operation. The proposed method shows an MSE of 2.05e1.2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{1.2}$$\end{document} PSNR of 28.4 dB, SSIM of 78.6, and entropy of 2.5 when the image is processed with salt and pepper noise.
... Recently, researchers have proposed a number of video privacy protection schemes, such as video encryption [8][9][10][11], video watermarking [12][13][14][15][16], and video steganography [17][18][19][20]. Among these schemes, video encryption is one of the more significant and secure approaches. ...
Compared to text and images, video can show information more vividly and intuitively via a moving picture; therefore, video is widely used in all walks of life. However, videos uploaded or stored in various video applications have not been treated with any protection, and these videos contain a lot of sensitive information that is more likely to be compromised. To solve this problem, video encryption schemes have been proposed. However, the main concern with existing video encryption schemes is that the private information in the encrypted video should be effectively protected, and, thus, the pixel distribution of the original video can be greatly damaged in the process of encryption, resulting in no or poor visual usability of the encrypted video. To this end, a novel color-video encryption scheme is proposed, which can effectively protect video privacy information while retaining certain visual information, thus enhancing the usability of encrypted videos. Firstly, the R, G, and B channels of the original color video are viewed as a whole for splitting. The dimensions of the blocks are three-dimensional, and permutation encryption is performed in three-dimensional blocks, which eliminates the redundancy of information between the video frame channels. Secondly, after permutation encryption, the channels of the video frame are separated, and then each channel is divided into blocks. The shape of the blocks is a square, and substitution encryption and permutation encryption operations are performed in turn. The whole encryption process is combined with the 2D-LSM chaotic system to improve the security of the scheme, as well as to reduce the time. Extensive experiments have been carried out, and the results show that the proposed scheme allows the encrypted video to retain rough visual information and, at the same time, effectively protects privacy, achieving the goal of encrypted video security and usability.
... Usually, there are two types of secret data that can be hidden inside the carrier, such as text, or image. Therefore, multiple steganography methods are applied for various types of communications [21,22]. ...
The exchange of information through social networking sites has become a major risk due to the possibility of obtaining millions of subscribers’ data at any time without the right. Multimedia security is a multifaceted field that involves various techniques and technologies to protect digital media in different contexts. As the technology evolves, so do the challenges and solutions related to multimedia security. Steganography plays a dominant role in covert communication over these social networking. In most modern adaptive steganography, the balancing between imperceptibility, payload, and security is a critical difficulty for image steganography. To this end, in this paper, we propose an improved image steganography method called IS-DGM based on a deep generative model (DGM) combined with hyper logistic map (HLM) encryption algorithm. IS-DGM consists of two strategies, steganography and recovery. In the first strategy, we have pre-processing and embedding networks. Before running the pre-processing network, the secret image is encoded using the HLM algorithm. During this phase, the encoded and the carrier images are utilized as inputs of the embedding network to boost concealment efficiency. In the second strategy, we have extraction and steganalysis networks. During this phase, the secret is extracted from the host image with the good visual quality as possible. Experimental outcomes indicate that the proposed method performs effectively in terms of perceptual quality and embedding capacity on five data sets, namely, ImageNet, CoCo2017, LFW, VoC2007, and VoC2012. In addition, it outperforms recent deep learning GAN hiding algorithms with respect to capacity, visual quality, and security. Thus, the proposed IS-DGM effectively balances good imperceptibility and increased capacity. Further, it maintains safety against histogram analysis, such as PVD analysis. Besides, the IS-DGM method increases resistance to the ROC curve analysis, including steganalysis algorithms, such as SRM, MaxSRM, Stegexpose, and Ye-Net.
... A review of steganographic techniques discloses a wide range of ways used to insert and hide information into digital carriers. Historically, conventional methods of steganography, as described in [6], entailed modifying the least significant bits of binary data in images, audio, or video files in order to conceal confidential information. ...
This chapter explores the diverse uses of steganography, a complex technique of hiding messages within everyday objects, across several sectors. The chapter focuses on the applications of steganography in finance and banking, healthcare, medical data security, and intellectual property. It examines the reasons, methods, advantages, and difficulties involved in adopting steganography. Furthermore, it elucidates the prospective trajectories and ramifications of this clandestine means of communication. The study also examines the function of steganography in organisational communication, highlighting its capacity to bolster security, facilitate hidden communication, and guarantee adherence to rules. The chapter ends with a thorough examination of the issues related to privacy, ethics, laws, and regulations that are associated with steganography. Lastly, it visualises the future path of this influential technology, highlighting the significance of openness, public knowledge, and cooperation for conscientious and moral advancement.
... Moreover, the imperceptibility and capacity of various steganographic methods are compared quantitatively in this work; however, non-steganographic methods are not considered. The review [10] is dedicated to steganography in video content. It covers the main theoretical basis, achievements, challenges in video steganography, and steganalysis; nevertheless, the paper focuses on video content, while in our work the focus is on images. ...
... Time and frequency information are used in Discrete Wavelet Transform (DWT) to segment signals into several narrower bands. One level of breakdown and reconstruction is shown in Figure 2. using the two-dimensional DWT(Discrete wavelet transform) (Kunhoth et al., 2023). Figure 2. displays the components of the LL, LH, HL, and HH sub-bands produced by 2D-DWT. ...
Steganography is a method for concealing confidential information in digital images in a way that is imperceptible to humans. However, existing steganographic techniques are frequently vulnerable to assaults such as steganalysis, which can detect the presence of hidden data. The purpose of this work is to develop a method for securely embedding text data within images while minimizing the visual impact on the carrier image. This research paper introduces an efficient method for image steganography by leveraging the GHM GHM(Geronimo-Hardin-Massopust) multiwavelet transform and n-bit Least Significant Bit (LSB) techniques. The proposed algorithm consists of three stages and for six different cases according to the altering of n- bits of the Least Significant Bit (LSB) embedding algorithm. Quality and safety of the stego-images were evaluated by experimental evaluations using metrics like Peak signal-to-noise ratio(PSNR), Root Mean Square Error (RMSE), and Structural Similarity Index Measure(SSIM). The results consistently demonstrated advantages of the suggested algorithm in terms of Peak signal-to-noise ratio(PSNR) about 24% improvement over the Least Significant Bit (LSB) techniques and 17% improvement over the the DWT (Discrete Wavelet Transform),also in terms of the Root Mean Square Error (RMSE), about 78% improvement over the Least Significant Bit (LSB) techniques and 67% improvement over the the DWT (Discrete Wavelet Transform) in average. The proposed approach significantly enhanced image quality while maintaining a high level of resemblance to the original image, showcasing its efficacy in preserving the underlying structure of the cover image.
... While most of the video steganography methods concentrate on enhancing embedding processes, one of the fundamental challenges is to determine optimal frames and regions within those frames for embedding the hidden data [3]. The selection of frames and regions plays a pivotal role in the effectiveness and robustness of the steganographic process. ...
Video steganography is a technique that involves hiding secret messages within a video while minimising any noticeable changes or distortions. The proposed work aims to embed multimedia data such as text, image, audio, or video inside a cover video in a secure and inconspicuous manner using different phases such as input preprocessing, frame selection, region selection and data embedding. To enhance the security of input data, the advanced encryption standard (AES) technique was used as a preprocessing step. To embed the encrypted data into the cover video with minimal distortion, key frames were selected using the histogram difference frame selection method. Robust regions were identified from the chosen key frames by applying principal component analysis (PCA) techniques as they offer better resistance to distortions caused by embedding data. Afterwards, the encrypted data was embedded into the robust regions using the adaptive inverted least significant bit-332 technique, which involves the modification of least significant bits of the pixel values. To ensure the receiver could accurately extract embedded information from the video, the indices of the key frames and robust regions were further embedded in random frames generated using a seed function. Experiments were conducted on different cover videos and input datasets to evaluate the performance of the proposed methodology using different quantitative metrics such as Peak signal-to-noise ratio (PSNR), Structural Similarity Index (SSIM), Normalised cross-correlation (NCC) and Bit error rate (BER), pixel embedding capacity and pay load capacity. The results showed that the proposed methodology achieved PSNR values above 60 dB, 50 dB and 40 dB for input data with sizes nearly 100 KB, 1 MB and 10 MB, respectively, and it outperformed the state-of-the-art methods in video steganography with an average pixel embedding capacity of 5.42 bits per pixel and payload embedding capacity of 45.1%.
... The article [47] begins by reviewing various raw domain-based video steganography methods, including spatial domain approaches such as least significant bits and transform domain-based methods such as discrete wavelet transform and dis-crete cosine transform. it reveals that multiple approaches can be used for video steganography or data hiding in video sequences, categorized based on the data hiding venue used in each method.However, developing an accurate steganography system poses many challenges. ...
This thesis proposes a solution to secure patients’ data and privacy usingsteganography (LSB Embedding) and cryptography (AES Encryption).To achieve this objective, the first step will involve introducing concepts suchas IoT, IoMT, steganography, and cryptography, along with their advantages andapplications. Additionally, these topics will be compared to the state of theart.Secondly, the most suitable coding program, Wolfram Mathematica, will beselected and compared with other programs to explain the reason for choosing it.Thirdly, two proposed flow charts and associated code for steganography andcryptography will be presented, covering the encryption-embedding to the decryption-extraction process. Fourthly, the flow charts and code will be used to create a tableof performance evaluation metrics, providing key metrics such as the quality of re-sults. Finally, the average PSNR, SSIM, and R results will be compared with otherarticles. Keywords:IoT,IoMT,LSB,ICT,AI,MitM,MRI,ECG,DCT,DWT.
... In order to encrypt a text, watermarking methods can be used in the text image, shifting the words in the middle space between them or shifting the background line to hide the data of the desired series in the background text, but this method which is considered in this research is the use of making changes in details. The majority of steganography research employs various types of cover media, such as images [3,31], video clips [4,32], and audio [5,33]. Nevertheless, text steganography is not commonly favored due to the challenges associated with identifying redundant bits in text files [6,7,34]. ...
Cryptography is an important technique for protecting information and communications. It utilizes codes to ensure that only the intended recipients can access and understand the content. In the field of computer science, cryptography relies on mathematical concepts and algorithms are used to transform messages into an encrypted form. In our current era of increasing internet and information technology, securing data has become a vital concern. The cryptography algorithms are used to protect the data from unauthorized users, the machine learning algorithms are used to identify the common similarity between the huge data set. Both the ideologies differ from one to another, but in this chapter discusses the connection between the giant models in a newer way. The evolution of cryptography, from ancient times to modern technology, is explored in this chapter.
