Biblio
In recent months, AI-synthesized face swapping videos referred to as deepfake have become an emerging problem. False video is becoming more and more difficult to distinguish, which brings a series of challenges to social security. Some scholars are devoted to studying how to improve the detection accuracy of deepfake video. At the same time, in order to conduct better research, some datasets for deepfake detection are made. Companies such as Google and Facebook have also spent huge sums of money to produce datasets for deepfake video detection, as well as holding deepfake detection competitions. The continuous advancement of video tampering technology and the improvement of video quality have also brought great challenges to deepfake detection. Some scholars have achieved certain results on existing datasets, while the results on some high-quality datasets are not as good as expected. In this paper, we propose new method with clustering-based embedding regularization for deepfake detection. We use open source algorithms to generate videos which can simulate distinctive artifacts in the deepfake videos. To improve the local smoothness of the representation space, we integrate a clustering-based embedding regularization term into the classification objective, so that the obtained model learns to resist adversarial examples. We evaluate our method on three latest deepfake datasets. Experimental results demonstrate the effectiveness of our method.
In practice, Defenders need a more efficient network detection approach which has the advantages of quick-responding learning capability of new network behavioural features for network intrusion detection purpose. In many applications the capability of Deep Learning techniques has been confirmed to outperform classic approaches. Accordingly, this study focused on network intrusion detection using convolutional neural networks (CNNs) based on LeNet-5 to classify the network threats. The experiment results show that the prediction accuracy of intrusion detection goes up to 99.65% with samples more than 10,000. The overall accuracy rate is 97.53%.
Personal privacy is an important issue when publishing social network data. An attacker may have information to reidentify private data. So, many researchers developed anonymization techniques, such as k-anonymity, k-isomorphism, l-diversity, etc. In this paper, we focus on graph k-degree anonymity by editing edges. Our method is divided into two steps. First, we propose an efficient algorithm to find a new degree sequence with theoretically minimal edit cost. Second, we insert and delete edges based on the new degree sequence to achieve k-degree anonymity.