In recent years, related research has focused on how to safely transfer and protect the privacy of images in social network services while providing easy access by authorized users. To safeguard privacy, we suggest an image encryption scheme that combines data hiding and image encryption. The proposed scheme successfully decrypts images after JPEG compression attacks and preserves the privacy of secret regions through the use of block scrambling encryption based on region selection. Simultaneously, the scheme can handle nonuniform secret regions and obtain more sensitive secret keys because of the incorporation of a chaotic system. The enhanced deep learning-based data-hiding technology reduces algorithm complexity by enabling the encryption position to be determined in the decryption phase without the need for any information or equipment. However, this approach also increases algorithm security, because only when the right secret data are extracted can they be decrypted successfully. According to the experimental findings, the proposed scheme can correctly decrypt images via JPEG compression while maintaining visually acceptable quality. The proposed scheme can achieve greater robustness against image processing algorithms and a wider secret key space than traditional schemes.

In recent years, advancements in deep Convolutional Neural Networks (CNNs) have brought about a paradigm shift in the realm of image super-resolution (SR). While augmenting the depth and breadth of CNNs can indeed enhance network performance, it often comes at the expense of heightened computational demands and greater memory usage, which can restrict practical deployment. To mitigate this challenge, we have incorporated a technique called factorized convolution and introduced the efficient Cross-Scale Interaction Block (CSIB). CSIB employs a dual-branch structure, with one branch extracting local features and the other capturing global features. Interaction operations take place in the middle of this dual-branch structure, facilitating the integration of cross-scale contextual information. To further refine the aggregated contextual information, we designed an Efficient Large Kernel Attention (ELKA) using large convolutional kernels and a gating mechanism. By stacking CSIBs, we have created a lightweight cross-scale interaction network for image super-resolution named “CSINet”. This innovative approach significantly reduces computational costs while maintaining performance, providing an efficient solution for practical applications. The experimental results convincingly demonstrate that our CSINet surpasses the majority of the state-of-the-art lightweight super-resolution techniques used on widely recognized benchmark datasets. Moreover, our smaller model, CSINet-S, shows an excellent performance record on lightweight super-resolution benchmarks with extremely low parameters and Multi-Adds (e.g., 33.82 dB@Set14 × 2 with only 248 K parameters).

Under the imbalanced dataset, the performance of the base-classifier, the computingmethod of weight of base-classifier and the selection method of the base-classifier have a great impact on the performance of the ensemble classifier. In order to solve above problem toimprove the generalization performance of ensemble classifier, a selective ensemble learning algorithm based on cluster under-sampling for imbalanced dataset is proposed. First, the proposed algorithm calculates the number K of under-sampling samples according to the relationship between class sample density. Then, we use the improved K-means clustering algorithm to under-sample the majority class samples and obtain K cluster centers. Then, all cluster centers (or the sample of the nearest cluster center) are regarded as new majority samples to construct a new balanced training subset combine with the minority class’s samples. Repeat those processes to generate multiple training subsets and get multiple base-classifiers. However, with the increasing of iterations, the number of base-classifiers increase, and the similarity among the base-classifiers will also increase. Therefore, it is necessary to select some base-classifier with good classification performance and large difference for ensemble. In the stage of selecting base-classifiers, according to the difference and performance of base-classifiers, we use the idea of maximum correlation and minimum redundancy to select base-classifiers. In the ensemble stage, G-mean or F-mean, which is selected to evaluate the classification performance of base-classifier forimbalanced dataset, is selected to compute the weight of each base-classifier, and then the weighted voting method is used for ensemble. Finally, the simulation results on the artificial dataset, UCI dataset and KDDCUP dataset show that the algorithm has good generalization performance on imbalanced dataset, especially on the dataset with high imbalance degree.