Most of the current malware detection methods running on Android are based on signature and cloud technologies leading to poor protection against new types of malware. Deep learning techniques take Android malware detection to a new level. Still, most deep learning-based Android malware detection methods are too inefficient or even unworkable on Android devices due to their high resource consumption. Therefore, this paper proposes MSFDroid, a lightweight multi-source fast Android malware detection model, which uses information from the internal files of the Android application package in several dimensions to build base models for ensemble learning. Meanwhile, this paper proposes an adaptive soft voting method by dynamically adjusting the weights of each base model to overcome the noise generated by traditional soft voting and thus improves the performance. It also proposes adaptive shrinkage convolutional unit that can dynamically adjust the convolutional kernel’s weight and the activation function’s threshold to improve the expressiveness of the CNN. The proposed method is tested on public datasets and on several real devices. The experimental results show that it achieves a better trade-off between performance and efficiency by significantly improving the detection speed while achieving a comparable performance compared to other deep learning methods.

Video virtual try-on methods aim to generate coherent, smooth, and realistic try-on videos, it matches the target clothing with the person in the video in a spatiotemporally consistent manner. Existing methods can match the human body with the clothing and then present it by the way of video, however it will cause the problem of excessive distortion of the grid and poor display effect at last. Given the problem, we found that was due to the neglect of the relationship between inputs lead to the loss of some features, while the conventional convolution operation is difficult to establish the remote information that is crucial in generating globally consistent results, restriction on clothing texture detail can lead to excessive deformation in the process of TPS fitting, make a lot of the try-on method in the final video rendering is not real. For the above problems, we reduce the excessive distortion of the garment during deformation by using a constraint function to regularize the TPS parameters; it also proposes a multimodal two-stage combinatorial Transformer: in the first stage, an interaction module is added, in which the long-distance relationship between people and clothing can be simulated, and then a better remote relationship can be obtained as well as contribute to the performance of TPS; in the second stage, an activation module is added, which can establish a global dependency, and this dependency can make the input important regions in the data are more prominent, which can provide more natural intermediate inputs for subsequent U-net networks. This paper’s method can bring better results for video virtual fitting, and experiments on the VVT dataset prove that the method outperforms previous methods in both quantitative and qualitative aspects.