In the field of frontier research, information security has received a lot of interest, but in the field of information security algorithm, the introduction of decimals makes it impossible to bypass the topic of calculation accuracy. This article creatively proposes the definition and related proofs of double parameters fractal sorting matrix (DPFSM). As a new matrix classification with fractal properties, DPFSM contains self-similar structures in the ordering of both elements and sub-blocks in the matrix. These two self-similar structures are determined by two different parameters. To verify the theory, this paper presents a type of $2\times 2$ DPFSM iterative generation method, as well as the theory, steps, and examples of the iteration. DPFSM is a space position transformation matrix, which has a better periodic law than a single parameter fractal sorting matrix (FSM). The proposal of DPFSM expands the fractal theory and solves the limitation of calculation accuracy on information security. The image encryption algorithm based on DPFSM is proposed, and the security analysis demonstrates the security. DPFSM has good application value in the field of information security.

Chaotic image encryption is widely used in the field of information security. This paper proposes a novel chaotic image encryption method with spiral-transform-based fractal sorting matrix (STFSM). First of all, the theory of STFSM with good scrambling effect is introduced, which has good irregularity and iterative. Then, the iterative algorithm and calculation example of STFSM are introduced. STFSM can be used as the map of spatial location transformations to implement the design of image encryption. Based on the complete STFSM theory and iterative algorithm, a chaotic image cryptosystem based on STFSM is proposed to achieve a good image encryption process. To test the security of the proposed algorithm, security tests and analyses such as entropy analysis, correlation analysis, resistance to differential attacks analysis, and robustness analysis are used for the proposed algorithm. The experimental analysis illustrates that the algorithm has a better encryption effect, whether using conventional tests or the attacks simulations described in this paper and can also effectively resist the attacks.

The complexity of chaotic systems, if used in information encryption, can determine the status of security. The paper proposes a novel image encryption scheme that uses chaotic maps and fuzzy numbers for the secure transmission of information. The encryption method combines logistic and sine maps to form the logistic sine map, as well as the fuzzy concept and the Hénon map to form the fuzzy Hénon map, in which these maps are used to generate secure secret keys, respectively. Additionally, a fuzzy triangular membership function is used to modify the initial conditions of the maps during the diffusion process. The encryption process involves scrambling the image pixels, summing adjacent row values, and XORing the result with randomly generated numbers from the chaotic maps. The proposed method is tested against various attacks, including statistical attack analysis, local entropy analysis, differential attack analysis, signal-to-noise ratio, signal-to-noise distortion ratio, mean error square, brute force attack analysis, and information entropy analysis, while the randomness number has been evaluated using the NIST test. This scheme also has a high key sensitivity, which means that a small change in the secret keys can result in a significant change in the encrypted image The results demonstrate the effectiveness of the proposed scheme in ensuring the secure transmission of information.

In today’s digital age, it is crucial to secure the flow of information to protect data and information from being hacked during transmission or storage. To address this need, we present a new image encryption technique that combines the Kronecker xor product, Hill cipher, and sigmoid logistic Map. Our proposed algorithm begins by shifting the values in each row of the state matrix to the left by a predetermined number of positions, then encrypting the resulting image using the Hill Cipher. The top value of each odd or even column is used to perform an xor operation with all values in the corresponding even or odd column, excluding the top value. The resulting image is then diffused using a sigmoid logistic map and subjected to the Kronecker xor product operation among the pixels to create a secure image. The image is then diffused again with other keys from the sigmoid logistic map for the final product. We compared our proposed method to recent work and found it to be safe and efficient in terms of performance after conducting statistical analysis, differential attack analysis, brute force attack analysis, and information entropy analysis. The results demonstrate that our proposed method is robust, lightweight, and fast in performance, meets the requirements for encryption and decryption, and is resistant to various attacks.

Existing deep learning-based reversible data hiding (RDH) predictors are affected by the difference of pixel complexity, which leads to the reduction of prediction accuracy. Therefore, this letter proposes an optimization framework tailored for RDH predictors, which integrates the local complexity of pixels into the predictor's regression optimization process. By analyzing the image's texture features, the framework adaptively determines the optimal prediction coefficients, thereby improving prediction accuracy. Notably, this optimization framework is versatile and can be applied to optimize other deep learning-based RDH predictors. Additionally, recognizing the critical role of interpolation strategies in RDH pixel prediction, we introduce a multi-scale fusion-enhanced interpolation network specifically designed for RDH, which integrates features across different scales to provide accurate reference pixels for subsequent predictions. Finally, experimental results demonstrate that the proposed method outperforms several advanced RDH predictors in terms of both prediction accuracy and embedding performance.