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 most adaptive video streaming systems adaptation decisions rely solely on the available network resources. As the content of a video has a large influence on the perception of quality our belief is that this is not sufficient. Thus, we have proposed a support service for content-aware video adaptation on mobile devices: Video Adaptation Service (VAS). Based on the content of a streamed video, the adaptation process is improved by setting a target quality level for a session based on an objective video quality metric. In this work, we demonstrate VAS and its advantages of a reduced data traffic by only streaming the lowest video representation which is necessary to reach a desired quality. By leveraging the content properties of a video stream, the system is able to keep a stable video quality and at the same time reduce the network load.