Xuncai Zhang

Traditional image encryption methods based on static permutation and diffusion are vulnerable to structural cryptanalysis and often exhibit limited robustness under imperfect communication conditions. To address these issues, this paper proposes a robust topology-evolving image encryption algorithm driven by complex hyperchaotic dynamics for secure visual data transmission. First, a two-dimensional Rosenbrock–Schwefel hyperchaotic map is constructed to generate high-quality pseudorandom sequences for both permutation and diffusion. Based on this map, a bidirectional oscillatory spatial permutation mechanism governed by a dynamic linked-list topology is developed. Unlike fixed-path permutation strategies, the proposed topology continuously evolves with the system state during image traversal, thereby increasing nonlinear path complexity and improving resistance to structural attacks. Furthermore, a plaintext-dependent adaptive diffusion mechanism is designed to enhance sensitivity to plaintext variations and produce a strong global avalanche effect. Experimental results demonstrate that the proposed algorithm achieves favorable encryption performance, with an information entropy of up to 7.9994, a Number of Pixels Change Rate (NPCR) of 99.6076%, and a Unified Average Changing Intensity (UACI) of 33.4683%. In addition, the algorithm maintains good recovery performance under cropping attacks and noise interference, indicating its robustness and applicability for secure image transmission in complex communication environments.

Traditional image encryption methods based on static permutation and diffusion are vulnerable to structural cryptanalysis and usually show limited resistance to channel interference. To address these problems, this paper proposes a robust topology-evolving image encryption algorithm for secure visual data transmission. The main contribution is a bidirectional oscillatory spatial permutation mechanism governed by a dynamic linked list topology. Unlike fixed-path scrambling, the proposed structure evolves continuously with the system state during image traversal, thereby increasing nonlinear path complexity and improving resistance to structural attacks. A plaintext-dependent adaptive diffusion mechanism is further designed to produce a strong global avalanche effect. In addition, a two-dimensional Rosenbrock-Schwefel hyperchaotic map is constructed to generate high-quality pseudorandom sequences for permutation and diffusion. Experimental results show that the encrypted images achieve a near-ideal information entropy of 7.9994, while the number of pixels change rate and unified average changing intensity reach 99.6076% and 33.4683%, respectively. The method also maintains good recovery performance under data cropping and noise interference, demonstrating its potential for secure image transmission in complex communication environments.

With the rapid proliferation of Internet of Things (IoT) devices, a vast volume of image data is continuously generated, raising significant concerns about data security and privacy. This paper proposes a novel image encryption scheme suitable for IoT environments, leveraging a complex three-dimensional (3D) chaotic system and an enhanced Josephus ring scrambling mechanism. The proposed chaotic system is developed by modifying the Liu chaotic model, expanding its parameter space and improving sequence randomness and unpredictability. Furthermore, the Josephus ring scrambling technique is refined by integrating the Fibonacci sequence and a main diagonal matrix model, replacing conventional element deletion with position swapping to increase the scrambling efficiency. To reinforce diffusion, a hybrid approach combining DNA rotation coding and dynamic DNA coding is applied, significantly increasing the complexity of pixel transformation. In addition, a dynamic ciphertext feedback mechanism employing four distinct chaotic sequences conceals key-related information and increases the cryptographic strength. Extensive experimental evaluations, including key space analysis, statistical assessments, sensitivity tests, and resistance to differential attacks, demonstrate the robustness, efficiency, and suitability of the proposed method for secure image transmission in IoT systems.

To address the security vulnerabilities encountered during the transmission of image data in Internet of Things (IoT) devices, this study introduces a novel encryption framework that integrates an enhanced four-dimensional hyperchaotic system with adaptive DNA coding. The proposed chaotic model is developed by augmenting the traditional Chen system with nonlinear feedback and coupling terms, resulting in improved dynamic behavior, which is validated via phase portraits, bifurcation diagrams, and NIST randomness assessments. A hybrid key initialization mechanism, driven by the SHA-256 hash of the plaintext image and external parameters, enables image-dependent key generation. A fourth-order Latin square is utilized to construct a dynamic DNA substitution matrix, while chaotic sequences guide the scrambling and cross-segmentation operations. Furthermore, a DNA-based XOR logic combined with bidirectional weighted diffusion enhances the confusion and diffusion strength of the encrypted image. Experimental evaluation indicates that the information entropy of the ciphertext image is 7.9993, while the Number of Pixels Change Rate (NPCR) and Unified Average Changing Intensity (UACI) reach 99.61% and 33.46% respectively. All test indicators demonstrate excellent performance, which proves that the encryption algorithm has high robustness and is applicable to the image encryption module for secure image transmission in the Internet of Things (IoT) environment.

DNA hybridization reaction is a significant technology in the field of semi-synthetic biology and holds great potential for use in biological computation. In this study, we propose a novel machine learning model based on a DNA hybridization reaction circuit. This circuit comprises a computation training component, a test component, and a learning algorithm. Compared to conventional machine learning models based on semiconductors, the proposed machine learning model harnesses the power of DNA hybridization reaction, with the learning algorithm implemented based on the unique properties of DNA computation, enabling parallel computation for the acquisition of learning results. In contrast to existing machine learning models based on DNA circuits, our proposed model constitutes a complete synthetic biology computation system, and utilizes the “dual-rail” mechanism to achieve the DNA compilation of the learning algorithm, which allows the weights to be updated to negative values. The proposed machine learning model based on DNA hybridization reaction demonstrates the ability to predict and fit linear functions. As such, this study is expected to make significant contributions to the development of machine learning through DNA hybridization reaction circuits.

To enhance the security level of image data transmission, this paper proposes a four-dimensional hyperchaotic image encryption scheme based on the combination of Thorp shuffle and pseudo dequeue. First, the nonlinear term of the classic four-dimensional Chen chaotic system is improved to construct an improved four-dimensional hyperchaotic system. Phase diagrams, Lyapunov exponents, and equilibrium points are used to analyze its dynamic characteristics. The system has rich dynamic behaviors, and its maximum Lyapunov exponent reaches 11.3. Second, the SHA-256 algorithm is used in combination with plain image information to generate an initial chaotic key. On the basis of this key, a pseudorandom sequence is generated by the chaotic system and used for scrambling and diffusion operations. An image encryption algorithm combined with chaotic sequences is subsequently designed, which includes index scrambling, a double-ended Thorp shuffle, and a diffusion step based on a multiplane bit operation. Finally, the performance of this algorithm is evaluated through experiments such as statistical analysis, differential attack analysis, and noise attack analysis. The information entropy of the cipher image can reach 7.9994, with the number of pixel change rates and the unified average change intensity being close to the ideal value, and the percentage of floating frequency can reach 63%. The results show that the algorithm proposed in this paper has high encryption efficiency and security and performs excellently in terms of anti-attack performance.

DNA, a new type of storage medium, has the advantages of high storage density, long-term stability, and low energy consumption. However, traditional storage schemes usually focus on high-density storage, neglecting local stability and decoding robustness. In this paper, we propose a coding scheme based on specific base sequences, which first constructs 48 “0-1” mapping rules and specific base sequences, encodes the data into base sequences with specific patterns, aims to improve the error correction and error detection ability of the data, and combines it with multivariate Huffman rotational coding to encode rule-indexed sequences. For errors during synthesis and sequencing, a method of comparison with specific base sequences is used to detect the location of the error and to recover the data in combination with multiple sequencing. The simulation results show that the local GC content of the encoded DNA sequences is between 45% and 55%, avoiding the generation of long homopolymers. With an error rate of 20%, the data can be recovered nearly completely with only 5 sequencing passes, and more errors can be tolerated by increasing the length of specific base sequences and the number of sequencing passes. This scheme improves the robustness of storage at an average storage density of 1.33 bit/nt and provides a new approach for the reliable storage of large-scale DNA data.

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.