With the increasing importance of securing images during network transmission, this paper introduces a novel image encryption algorithm that integrates a 3D chaotic system with V-shaped scrambling techniques. The proposed method begins by constructing a unique 3D chaotic system to generate chaotic sequences for encryption. These sequences determine a random starting point for V-shaped scrambling, which facilitates the transformation of image pixels into quaternary numbers. Subsequently, four innovative bit-level scrambling strategies are employed to enhance encryption strength. To further improve randomness, DNA encoding is applied to both the image and chaotic sequences, with chaotic sequences directing crossover and DNA operations. Ciphertext feedback is then utilized to propagate changes across the image, ensuring increased complexity and security. Extensive simulation experiments validate the algorithm’s robust encryption performance for grayscale images, yielding uniformly distributed histograms, near-zero correlation values, and an information entropy value of 7.9975, approaching the ideal threshold. The algorithm also features a large key space, providing robust protection against brute force attacks while effectively resisting statistical, differential, noise, and cropping attacks. These results affirm the algorithm’s reliability and security for image communication and transmission.

The continuous evolution of information technology underscores the growing emphasis on data security. In the realm of medical imaging, various diagnostic images represent the privacy of individuals, and the potential repercussions of their unauthorized disclosure are substantial. Therefore, this study introduces a novel chaotic system (TLCMCML) and employs it to propose a multi-image medical image encryption algorithm. To simultaneously augment security and optimize encryption efficiency, we undertake a dual-pronged approach. Firstly, we identify the regions of interest (ROIs) within individual medical images, subsequently applying an independent scrambling technique based on an odd-even interleaving configuration. Secondly, we integrate all medical images through horizontal concatenation, forming a comprehensive large-scale image, upon which we implement a synchronized bit-level permutation-diffusion encryption mechanism. Following extensive testing, enhancements were verified across various metrics including information entropy analysis, adjacent pixel correlation examination, differential attack simulations, and robustness assessments, thereby attesting to the exceptional encryption efficacy of the proposed algorithm.

This article proposes an image compression and encryption algorithm based on 3-D logistic-sine-memristor coupled map (3D-LSMCM), compressed sensing (CS), and the game-of-life scrambling with semi-tensor product (STP-GoL) to solve the problems of computational redundancy and security vulnerability in image transmission. First, the 3D-LSMCM is initialized by using a hash function to generate a random chaotic sequence iteratively, and it improves key unpredictability. Second, the semi-tensor product (STP) measurement matrix constructed from these sequences significantly reduces storage requirements in CS while supporting lossless compression through arithmetic coding. STP technology enables flexible matrix dimension design, effectively lowering computational costs. Finally, STP is used to convert logical operations into matrix operations in the entire encryption process. The cryptographic technique consists of the STP-GoL scrambling, chaotic global scrambling with STP (STP-CG), and the chain XOR diffusion based on STP (STP-CXOR). Furthermore, theoretical analysis indicates that the computational complexity of this algorithm is expected to be reduced by approximately 28% compared with recent studies, while the average information entropy of the ciphertext reaches 7.9987. Overall, this study proposes a novel, secure, and efficient image compression and encryption method, with its source code publicly available at https://github.com/Ssshou7/STP-GoL

With increasing concerns over privacy protection, existing methods face significant challenges in securing images containing multiple faces. This paper addresses this issue by proposing a multiple-face encryption framework that integrates precise face localization with a robust encryption scheme. For localization, an enhanced YOLOv8 model is developed, where the EMA attention mechanism captures fine-grained features, the BiFPN module enables efficient multi-scale fusion, and the WIoU loss reduces training errors. These improvements substantially enhance detection performance on face datasets, ensuring reliable identification of multiple facial regions. For encryption, a novel superposed algorithm based on a non-adjacent logistic-dynamic coupled map lattice is introduced, effectively overcoming the limitations of the Arnold cat map in handling rectangular images. Moreover, a two-way diffusion mechanism is incorporated to resist chosen-plaintext attacks, thereby enhancing security. Experimental results demonstrate that the proposed method achieves strong robustness against various cryptographic attacks, while providing a promising direction for advancing research in face image cryptography.