A new color image encryption technique using DNA computing and Chaos-based substitution box

In many cases, images contain sensitive information and patterns that require secure processing to avoid risk. It can be accessed by unauthorized users who can illegally exploit them to threaten the safety of people’s life and property. Protecting the privacies of the images has quickly become one of the biggest obstacles that prevent further exploration of image data. In this paper, we propose a novel privacy-preserving scheme to protect sensitive information within images. The proposed approach combines deoxyribonucleic acid (DNA) sequencing code, Arnold transformation (AT), and a chaotic dynamical system to construct an initial S-box. Various tests have been conducted to validate the randomness of this newly constructed S-box. These tests include National Institute of Standards and Technology (NIST) analysis, histogram analysis (HA), nonlinearity analysis (NL), strict avalanche criterion (SAC), bit independence criterion (BIC), bit independence criterion strict avalanche criterion (BIC-SAC), bit independence criterion nonlinearity (BIC-NL), equiprobable input/output XOR distribution, and linear approximation probability (LP). The proposed scheme possesses higher security wit NL = 103.75, SAC ≈ 0.5 and LP = 0.1560. Other tests such as BIC-SAC and BIC-NL calculated values are 0.4960 and 112.35, respectively. The results show that the proposed scheme has a strong ability to resist many attacks. Furthermore, the achieved results are compared to existing state-of-the-art methods. The comparison results further demonstrate the effectiveness of the proposed algorithm.

Measuring Avalanche Properties on RC4 Stream Cipher Variants

In the last three decades, the RC4 has been the most cited stream cipher, due to a large amount of research carried out on its operation. In this sense, dissimilar works have been presented on its performance, security, and usability. One of the distinguishing features that stand out the most is the sheer number of RC4 variants proposed. Recently, a weakness has been reported regarding the existence of statistical dependence between the inputs and outputs of the RC4, based on the use of the strict avalanche criterion and the bit independence criterion. This work analyzes the influence of this weakness in some of its variants concerning RC4. The five best-known variants of RC4 were compared experimentally and classified into two groups according to the presence or absence of such a weakness.

Hilbert–Schmidt Independence Criterion Regularization Kernel Framework on Symmetric Positive Definite Manifolds

Image recognition tasks involve an increasingly high amount of symmetric positive definite (SPD) matrices data. SPD manifolds exhibit nonlinear geometry, and Euclidean machine learning methods cannot be directly applied to SPD manifolds. The kernel trick of SPD manifolds is based on the concept of projecting data onto a reproducing kernel Hilbert space. Unfortunately, existing kernel methods do not consider the connection of SPD matrices and linear projections. Thus, a framework that uses the correlation between SPD matrices and projections to model the kernel map is proposed herein. To realize this, this paper formulates a Hilbert–Schmidt independence criterion (HSIC) regularization framework based on the kernel trick, where HSIC is usually used to express the interconnectedness of two datasets. The proposed framework allows us to extend the existing kernel methods to new HSIC regularization kernel methods. Additionally, this paper proposes an algorithm called HSIC regularized graph discriminant analysis (HRGDA) for SPD manifolds based on the HSIC regularization framework. The proposed HSIC regularization framework and HRGDA are highly accurate and valid based on experimental results on several classification tasks.

A Novel Lightweight Block Encryption Algorithm Based on Combined Chaotic S-Box

Due to high computational cost, traditional encryption algorithms are not suitable for the environments in which resources are limited. In view of the above problem, we first propose a combined chaotic map to increase the chaotic interval and Lyapunov exponent of the existing one-dimensional chaotic maps. Then, an S-box based on the proposed combined chaotic map is constructed. The performances of the designed S-box, such as bijection, nonlinearity, strict avalanche criteria, differential uniformity, the bits independence criterion, and the linear approximation probability, are tested to show that it has better cryptographic performances. Finally, we present a lightweight block encryption algorithm by using the above S-box. The algorithm is based on the generalized Feistel structure and SPN structure. In addtion, the processes of encryption and decryption of our algorithm are almost the same, which reduces the complexity of algorithm implementation. The experimental results show that the proposed encryption algorithm meets the requirements of lightweight algorithms and has good cryptographic characteristics.

Multivariate information fusion for identifying antifungal peptides with Hilbert-Schmidt Independence Criterion

Background: Antifungal peptides (AFP) have been found to be effective against many fungal infections. Objective: However, it is difficult to identify AFP. Therefore, it is great practical significance to identify AFP via machine learning methods (with sequence information). Method: In this study, a Multi-Kernel Support Vector Machine (MKSVM) with Hilbert-Schmidt Independence Criterion (HSIC) is proposed. Proteins are encoded with five types of features (188-bit, AAC, ASDC, CKSAAP, DPC), and then construct kernels using Gaussian kernel function. HSIC are used to combine kernels and multi-kernel SVM model is built. Results: Our model performed well on three AFPs datasets and the performance is better than or comparable to other state-of-art predictive models. Conclusion: Our method will be a useful tool for identifying antifungal peptides.

Hilbert–Schmidt Independence Criterion Subspace Learning on Hybrid Region Covariance Descriptor for Image Classification

The region covariance descriptor (RCD), which is known as a symmetric positive definite (SPD) matrix, is commonly used in image representation. As SPD manifolds have a non-Euclidean geometry, Euclidean machine learning methods are not directly applicable to them. In this work, an improved covariance descriptor called the hybrid region covariance descriptor (HRCD) is proposed. The HRCD incorporates the mean feature information into the RCD to improve the latter’s discriminative performance. To address the non-Euclidean properties of SPD manifolds, this study also proposes an algorithm called the Hilbert-Schmidt independence criterion subspace learning (HSIC-SL) for SPD manifolds. The HSIC-SL algorithm is aimed at improving classification accuracy. This algorithm is a kernel function that embeds SPD matrices into the reproducing kernel Hilbert space and further maps them to a linear space. To make the mapping consider the correlation between SPD matrices and linear projection, this method introduces global HSIC maximization to the model. The proposed method is compared with existing methods and is proved to be highly accurate and valid by classification experiments on the HRCD and HSIC-SL using the COIL-20, ETH-80, QMUL, face data FERET, and Brodatz datasets.