Embedding Error Based Data Hiding in Color Images for Distortion Tolerance

In this paper, a data hiding scheme with distortion tolerance for color image is proposed. Data hiding is used to embed secret information into the cover image for secure transmission and protecting copyright. The secret information feasibly a text or an image. To protect the copyright of a true color image, a signature (a watermark), which is represented by a sequence of binary data, is embedded in the color image. In this proposed scheme, we first calculate the embedding error between the cover image and the secret information. Based on this embedding error, the stego image is computed then the embedded data are extracted by the extraction procedure. This scheme can tolerate some distortion such us salt and pepper noise, Gaussian noise, uniform noise, and JPEG lossy compression when transmitting a stego image through any network. Experimental results and discussions reveal that the proposed scheme tolerates those distortions with acceptable image quality

Reversible data hiding scheme based on 3-Least significant bits and mix column transform

Steganography is the science of hiding a message signal in a host signal, without any perceptual distortion of the host signal. Using steganography, information can be hidden in the carrier items such as images, videos, sounds files, text files, while performing data transmission. In image steganography field, it is a major concern of the researchers how to improve the capacity of hidden data into host image without causing any statistically significant modification. In this work, we propose a reversible steganography scheme which can hide large amount of information without affecting the imperceptibility aspect of the stego-image and at the same time, it increases the security level of the system through using different method for embedding based on distinct type of transform, called Mix Column Transform. Our experimental results prove the ability of our proposed scheme in balancing among the three critical properties: capacity, security, and imperceptibility

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

Symmetry-Adapted Machine Learning for Information Security

Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis