KEYWORDS: Education and training, Video, Convolution, Data modeling, Video compression, Machine learning, Data analysis, Matrices, Feature extraction, Performance modeling
The emergence of DeepFake poses serious risks to data privacy and social stability. We propose an end-to-end DeepFake video detection method based on a dense dynamic convolutional neural network (CNN) to address the poor performance of DeepFake video detection on complex compression formats and datasets of different forgery methods. In this method, extracted face images are clustered and cleaned by cosine similarity, and face images are expanded through data augmentation to improve data diversity. Dynamic dense blocks are incorporated in a CNN to address optimization difficulties in deep neural networks, and an attention mechanism further improves generalization power. Convolution kernel pruning increases processing speed by effectively reducing the computational needs due to dynamic convolution. Experiments demonstrate that this method has better results on DeepFake video detection across compression rates and datasets compared to other network models.
In order to enhance the isolation security of 5G cryptographic computing, a network slice deployment method for cryptographic computing isolation was proposed in this paper. Firstly, based on hardware cryptographic virtualization technology, the Network Function Virtualization (NFV) architecture based on cryptographic card virtualization was designed in this paper. By analyzing the characteristics of cryptographic calculation of different Virtual Network Function (VNF) requirement in 5G network slices, the allocation policies of cryptographic resources are set. Then, the deployment method was established as a mixed integer programming model, taking the deployment cost as the objective function, and reducing the deployment cost of network slice by minimizing the objective function. Finally, genetic algorithm is used to simulate the model. Experiments show that the proposed method reduces the deployment cost on the premise of ensuring security.
Generative adversary networks (GAN) have recently led to highly realistic synthesized image. For the current GAN-synthesized faces detection methods exist false prediction if the real faces with angles or occlusion. This paper proposes a GAN-synthesized faces detection method based on Deep Alignment Network (DAN), which improve prediction accuracy of real faces by makes the locations of facial landmark points more precise. Our method first uses DAN to obtain the locations of facial landmark points of real and synthesized faces; then the landmark points are converted into feature vectors by principal component analysis (PCA); finally, input feature vectors to the constructed Support Vector Machine (SVM)classifier for training. Experimental results show that our method achieves better performance than other method under face with angles or occlusion.
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