This paper presents an advanced encryption algorithm specifically designed to enhance the security of volumetric medical image data, crucial for the Internet of Medical Things (IoMT). The algorithm encrypts a stack of 256 images, each with dimensions of 256×256 pixels, through a meticulous multi-stage process. It begins by segmenting an image cube into Red, Green, and Blue channels, which are then encrypted through three phases: XOR operations with keys from an 8D hyperchaotic system, substitution with S-boxes derived from Gold sequences, and a final transformation using Fibonacci Q-matrices. This approach significantly improves security by improving entropy, decreasing cross-correlation, and strengthening resistance to statistical attacks, with various cryptographic seeds used at each stage to enhance the robustness of the encryption. Specific performance evaluation metrics include a pixel cross-correlation of approximately 0, an NPCR of 99.61%, a UACI of 34.45%, an entropy of 7.998, and a key space that exceeds 22870. Extensive cryptographic assessments confirm the effectiveness of this algorithm, making it a vital tool for securing medical image data within IoMT, ensuring safe transmission and storage in healthcare systems.

Abstract This research work presents a novel secured spatial steganography algorithm consisting of three stages. In the first stage, a secret message is divided into three parts, each is encrypted using a tan logistic map encryption key with a unique seed value. In the second stage, the encrypted parts are transformed into quick response codes, serving as a layer of channel coding. Subsequently, the quick response codes are decoded back into bit‐streams. To enhance security, a uniquely‐seeded Mersenne Twister key is generated and employed to apply DNA coding onto each bit‐stream. The resulting bit‐streams are then embedded in the least significant bits of the RGB channels of a cover image. Finally, the RGB channels are merged to form a single stego image. A comprehensive set of experimental analyses is conducted to evaluate the performance of the proposed secure steganography algorithm. The experimental results demonstrate the algorithm's robustness against various attacks and its ability to achieve high embedding capacity while maintaining imperceptibility. The proposed algorithm offers a promising solution for secure information hiding in the spatial domain, with potential applications in areas such as data transmission, digital forensics, and covert communication.

The ever-evolving nature of the Internet and wireless communications, as well as the production of huge amounts of multimedia every day has created a dire need for their security. In this paper, an image encryption technique that is based on 3 stages is proposed. The first stage makes use of DNA encoding. The second stage proposed and utilizes a novel S-box that is based on the Mersenne Twister and a linear descent algorithm. The third stage employs the Tent chaotic map. The computed performance evaluation metrics exhibit a high level of achieved security.

In this work, two enhancement methods are proposed to speed up junction detection performed by the JUDOCA detector. The first enhancement method minimizes the number of junction candidates on which the circular kernel is applied. This is achieved by introducing a suppression technique that takes both the thin and thick edge images into consideration. The second method works on relaxing the step of checking the edge continuity. Instead of traversing the edge pixel by pixel, using Bresenham's algorithm, sample pixels along the edge are considered. Test results show that an enhancement factor of 62% to 75% in runtime can be reached as a result of applying both enhancements.