Time series anomaly detection is very important to ensure the security of industrial control systems (ICSs). Many algorithms have performed well in anomaly detection. However, the performance of most of these algorithms decreases sharply with the increase in feature dimension. This paper proposes an anomaly detection scheme based on Graph Attention Network (GAT) and Informer. GAT learns sequential characteristics effectively, and Informer performs excellently in long time series prediction. In addition, long-time forecasting loss and short-time forecasting loss are used to detect multivariate time series anomalies. Short-time forecasting is used to predict the next time value, and long-time forecasting is employed to assist the short-time prediction. We conduct a large number of experiments on industrial control system datasets SWaT and WADI. Compared with most advanced methods, we achieve competitive results, especially on higher-dimensional datasets. Moreover, the proposed method can accurately locate anomalies and realize interpretability.

Most of the voice spoofing detection methods are designed for specific kinds of spoofing attacks, synthetic or replay. In practice, however, there is no prior information about these two kinds of spoofing attacks. To this end, this paper proposes a generalized voice spoofing detection method based on integral knowledge amalgamation to detect jointly synthetic attacks and replay attacks. Two amalgamation mechanisms, feature amalgamation and structure amalgamation, are designed from different perspectives, so that the model can generalize better and run fast. Specifically, the feature amalgamation transfers the high-level sematic knowledge from two teacher models to the compact model. The structure amalgamation employs the adversarial learning to ensure the global structure consistency of two teacher models and a student model. In addition, the feature matching loss is introduced to capture the distinctive features of synthetic attacks and replay attacks. We conduct extensive experiments on logical access (LA) scenario and physical access (PA) scenario of ASVspoof 2019 dataset to verify the validity of the proposed method. The experimental results show that compared with the most advanced generalized voice spoofing detection methods, the proposed method achieves a comparable or even better performance. In particular, our method gains the state-of-the-art detection capability on LA scenario. Moreover, our method achieves similar or even outstanding detectability when compared with specialized anti-spoofing methods.

Voice spoofing detection is a technique for enhancing the security of automatic speaker verification system, but the existing research still faces problems such as weak detection capability and expensive computation. To address these problems, this work presents a lightweight voice anti-spoofing method by using improved one-class learning DOC-Softmax and knowledge distillation. The main idea of DOC-Softmax is to learn a feature space where the genuine samples have a compact space and the spoofing samples are parted from the bona fide space by a certain interval. And the dispersion loss is introduced for spoofing samples to cover the whole spoofing space as much as possible. Moreover, a lightweight voice spoofing detection model is designed to speed up inference, and the knowledge distillation is employed to improve representation power of the lightweight model. Without any data augmentation and ensemble learning, a series of experiments are conducted on LA and PA scenarios of the ASVspoof 2019 dataset, and the experimental results indicate that the proposed method performs better than most existing voice anti-spoofing methods.

With the advent of the artificial intelligence (AI) era, the beauty camera is widely used, and makeup transfer has attracted increasing attention. In this paper, we propose an adaptive makeup transfer based on the bat algorithm to solve the problem that only a single makeup effect can be transferred. According to the characteristics of makeup style, the algorithm optimizes the weight value to get the appropriate makeup lightness by using the adaptive method. The improved algorithm can not only help to get the optimal weight values in the process of transferring the same makeup style to different targets, but also to transfer different makeup styles to the same target. Moreover, this algorithm can choose the most suitable makeup style and also the most appropriate lightness for a certain person. Experimental results show that the algorithm proposed in the paper has a better effect than the existing algorithm of makeup transfer, and the algorithm can provide users with a suitable makeup style and appropriate lightness.

The number of solutions obtained is too large to provide a set of solutions with good performance in the nearby area of the true Pareto front when problem-specific preferences are unavailable. Therefore, this paper proposes a knee point-driven many-objective pigeon-inspired optimization algorithm (KnMAPIO). An environmental selection strategy based on knee-oriented dominance is proposed to improve selection pressure and population diversity. In addition, a new velocity updating equation with Gaussian distribution, Cauchy distribution and Levy distribution is proposed in this paper to provide new search directions and reduce the possibility of falling into local optima. Two types of experiments are carried out in this paper: one is to compare the proposed method with four other algorithms on the knee-oriented benchmark PMOPs to verify the algorithm’s performance in detecting the knee points and the knee region; another is to compare the proposed method with eight other state-of-the-art algorithms on the classic benchmark DTLZ and WFG. The results of both experiments verify the effectiveness of the proposed algorithm and the ability to approximate to the true Pareto front.

Most fraud detection algorithms identify the anomaly by learning from a small number of existing fraud samples, therefore they are often ineffective when deal with complex and unknown situations. This paper proposes a new one-class classification model called one-class adversarial fraud detection nets with class specific representations (CS-OCAN), which consists of modified autoencoders and Complementary generative adversarial networks (GAN). Firstly, the two-iteration framework is designed to make reasonable use of the reference data generated by Complementary GAN. Secondly, an additional loss function is added in the latent space of the autoencoder, which transforms the semi-supervised problem into a supervised problem that aims to maximize inter-class distances between two classes and minimize intra-class variances. We have conducted experiments on UMD Wikipedia dataset and Credit card fraud detection dataset. Experimental results show that our method CS-OCAN has significantly improved the detection accuracy and stability compared with the state-of-the-art one-class classification models.