There is an increasing interest in enhancing the quality of low-resolution (LR) facial images for various social life applications. Existing methods often use domain-specific prior knowledge, which is effective in improving the face super-resolution model's performance. However, it is challenging to obtain rich and accurate prior information from LR inputs in real-world scenarios, which can limit the robustness and generalization ability of the developed face super-resolution model. In this paper, a multisource reference-based face super-resolution Network, namely MSRNet, is proposed. Without considering the prior knowledge of faces, the network can reconstruct a LR face image with a magnitude factor of 8 under the guidance of multiple reference face images of different identities. By constructing an “appearance-alike” reference data set Face_Ref, the designed MSRNet aims to fully exploit the local and spatially similar high frequency information between the distinct references and the current face. More specifically, to effectively combine the information from multiple references, a cross-scale and cross-space feature fusion mechanism is introduced for external and internal references, and then the enhanced local semantics are finally incorporated into the high-resolution face reconstruction. The robustness of face image super-resolution is increased compared to current correlation approaches, since it not only eliminates the need for face prior knowledge but also avoids performing alignment operations on reference faces with multiple expressions and different poses. Experimental results show that the proposed model is able to produce results for face super-resolution that are satisfying and dependable and outperforms the state-of-the-art methods in terms of visual perceptual quality and quantity evaluation.

In recent years, the number and variety of malicious mobile apps have increased drastically, especially on Android platform, which brings insurmountable challenges for malicious app detection. Researchers endeavor to discover the traces of malicious apps using network traffic analysis. In this study, we combine network traffic analysis with machine learning methods to identify malicious network behavior, and eventually to detect malicious apps. However, most network traffic generated by malicious apps is benign, while only a small portion of traffic is malicious, leading to an imbalanced data problem when the traffic model skews towards modeling the benign traffic. To address this problem, we introduce imbalanced classification methods, including the synthetic minority oversampling technique (SMOTE) + support vector machine (SVM), SVM cost-sensitive (SVMCS), and C4.5 cost-sensitive (C4.5CS) methods. However, when the imbalance rate reaches a certain threshold, the performance of common imbalanced classification algorithms degrades significantly. To avoid performance degradation, we propose to use the imbalanced data gravitation-based classification (IDGC) algorithm to classify imbalanced data. Moreover, we develop a simplex imbalanced data gravitation classification (S-IDGC) model to further reduce the time costs of IDGC without sacrificing the classification performance. In addition, we propose a machine learning based comparative benchmark prototype system, which provides users with substantial autonomy, such as multiple choices of the desired classifiers or traffic features. Using this prototype system, users can compare the detection performance of different classification algorithms on the same data set, as well as the performance of a specific classification algorithm on multiple data sets.