Optimization of medical image steganography using n-decomposition genetic algorithm

Protecting patients' confidential information is a critical concern in medical image steganography. The Least Significant Bits (LSB) technique has been widely used for secure communication. However, it is susceptible to imperceptibility and security risks due to the direct manipulation of pixels, and ASCII patterns present limitations. Consequently, sensitive medical information is subject to loss or alteration. Despite attempts to optimize LSB, these issues persist due to (1) the formulation of the optimization suffering from non-valid implicit constraints, causing inflexibility in reaching optimal embedding, (2) lacking convergence in the searching process, where the message length significantly affects the size of the solution space, and (3) issues of application customizability where different data require more flexibility in controlling the embedding process. To overcome these limitations, this study proposes a technique known as an n-decomposition genetic algorithm. This algorithm uses a variable-length search to identify the best location to embed the secret message by incorporating constraints to avoid local minimum traps. The methodology consists of five main phases: (1) initial investigation, (2) formulating an embedding scheme, (3) constructing a decomposition scheme, (4) integrating the schemes' design into the proposed technique, and (5) evaluating the proposed technique's performance based on parameters using medical datasets from kaggle.com. The proposed technique showed resistance to statistical analysis evaluated using Reversible Statistical (RS) analysis and histogram. It also demonstrated its superiority in imperceptibility and security measured by MSE and PSNR to Chest and Retina datasets (0.0557, 0.0550) and (60.6696, 60.7287), respectively. Still, compared to the results obtained by the proposed technique, the benchmark outperforms the Brain dataset due to the homogeneous nature of the images and the extensive black background. This research has contributed to genetic-based decomposition in medical image steganography and provides a technique that offers improved security without compromising efficiency and convergence. However, further validation is required to determine its effectiveness in real-world applications

A Detection Method for Text Steganalysis Using Evolution Algorithm (EA) Approach

The ability of sending a secret message through a network nowadays has become a more challenging and complex process. One of the reasons why we need tools to detect the hidden message is because of security and safety. Secret messages can be both for good or bad, and subversive groups have been known to send secret messages to coordinate their terrorist activities. Thus,a technique such as steganalysis is one method example to detect a secret message. Much of the technical steganalysis work had been carried out on image, video, and audio steganalysis, but in any agency or organisation, all business documents generally uses the natural language in text form or document form. Therefore, this research employed a detection factor based on the evolution algorithm method for text steganalysis. The aim of this project was to detect a hidden message in an observed message using text steganalysis

Constructing HVS-Based Optimal Substitution Matrix Using Enhanced Differential Evolution

Least significant bit (LSB) substitution is a method of information hiding. The secret message is embedded into the last k bits of a cover-image in order to evade the notice of hackers. The security and stego-image quality are two main limitations of the LSB substitution method. Therefore, some researchers have proposed an LSB substitution matrix to address these two issues. Finding the optimal LSB substitution matrix can be conceptualized as a problem of combinatorial optimization. In this paper, we adopt a different heuristic method based on other researchers’ method, called enhanced differential evolution (EDE), to construct an optimal LSB substitution matrix. Differing from other researchers, we adopt an HVS-based measurement as a fitness function and embed the secret by modifying the pixel to a closest value rather than simply substituting the LSBs. Our scheme extracts the secret by modular operations as simple LSB substitution does. The experimental results show that the proposed embedding algorithm indeed improves imperceptibility of stego-images substantially

Text steganalysis using evolution algorithm approach

This study presents a new alternative of steganalysis method in order to detect hidden messages in text steganalysis called Evolution Detection Steganalysis System (EDSS) based on the evolution algorithm approach under Java Genetic Algorithms Package (JGAP). The result of the EDSS can be divided into two groups based on fitness values which are good fitness and bad fitness. Hopefully, this study can produce a good idea to other researchers for understanding the text steganalysis in order to develop a steganalysis system that can contribute a better performance in other domains

Fitness value based evolution algorithm approach for text steganalysis model

In this paper, we present a new alternative method for text steganalysis based on an evolution algorithm, implemented using the Java Evolution Algorithms Package (JEAP).The main objective of this paper is to detect the existence of hidden messages ased on fitness values of a text description.It is found that the detection performance has been influenced by two groups of fitness values which are good fitness value and bad fitness value. This paper provides a valuable insight into the development and enhancement of the text steganalysis domain

Air Force Institute of Technology Research Report 2000

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, and Engineering Physics

On the Construction and Cryptanalysis of Multi-Ciphers

In this compilational work, we combine various techniques from classical cryptography and steganography to construct ciphers that conceal multiple plaintexts in a single ciphertext. We name these multi-ciphers . Most notably, we construct and cryptanalyze a Four-In-One-Cipher: the first cipher which conceals four separate plaintexts in a single ciphertext. Following a brief overview of classical cryptography and steganography, we consider strategies that can be used to creatively combine these two fields to construct multi-ciphers. Finally, we cryptanalyze three multi-ciphers which were constructed using the techniques described in this paper. This cryptanalysis relies on both traditional algorithms that are used to decode classical ciphers and new algorithms which we use to extract the additional plaintexts concealed by the multi-ciphers. We implement these algorithms in Python, and provide code snippets. The primary goal of this work is to inform others who might be otherwise unfamiliar with the fields of classical cryptography and steganography from a new perspective which lies at the intersection of these two fields. The ideas presented in this paper could prove useful in teaching cryptography, statistics, mathematics, and computer science to future generations in a unique, interdisciplinary fashion. This work might also serve as a source of creative inspiration for other cipher-making, code-breaking enthusiasts

Efficient and Robust Video Steganography Algorithms for Secure Data Communication

Over the last two decades, the science of secretly embedding and communicating data has gained tremendous significance due to the technological advancement in communication and digital content. Steganography is the art of concealing secret data in a particular interactive media transporter such as text, audio, image, and video data in order to build a covert communication between authorized parties. Nowadays, video steganography techniques are important in many video-sharing and social networking applications such as Livestreaming, YouTube, Twitter, and Facebook because of noteworthy developments in advanced video over the Internet. The performance of any steganography method, ultimately, relies on the imperceptibility, hiding capacity, and robustness against attacks. Although many video steganography methods exist, several of them lack the preprocessing stages. In addition, less security, low embedding capacity, less imperceptibility, and less robustness against attacks are other issues that affect these algorithms. This dissertation investigates and analyzes cutting edge video steganography techniques in both compressed and raw domains. Moreover, it provides solutions for the aforementioned problems by proposing new and effective methods for digital video steganography. The key objectives of this research are to develop: 1) a highly secure video steganography algorithm based on error correcting codes (ECC); 2) an increased payload video steganography algorithm in the discrete wavelet domain based on ECC; 3) a novel video steganography algorithm based on Kanade-Lucas-Tomasi (KLT) tracking and ECC; 4) a robust video steganography algorithm in the wavelet domain based on KLT tracking and ECC; 5) a new video steganography algorithm based on the multiple object tracking (MOT) and ECC; and 6) a robust and secure video steganography algorithm in the discrete wavelet and discrete cosine transformations based on MOT and ECC. The experimental results from our research demonstrate that our proposed algorithms achieve higher embedding capacity as well as better imperceptibility of stego videos. Furthermore, the preprocessing stages increase the security and robustness of the proposed algorithms against attacks when compared to state-of-the-art steganographic methods