Visible to the public Deepfake Detection with Clustering-based Embedding Regularization

TitleDeepfake Detection with Clustering-based Embedding Regularization
Publication TypeConference Paper
Year of Publication2020
AuthorsZhu, K., Wu, B., Wang, B.
Conference Name2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC)
Date PublishedJuly 2020
ISBN Number978-1-7281-9558-2
KeywordsAI-synthesized face swapping videos, clustering-based, clustering-based embedding regularization, DeepFake, deepfake datasets, deepfake detection, deepfake detection competitions, deepfake video detection, detection accuracy, Embedded systems, face recognition, face swapping, false video, feature extraction, Human Behavior, human factors, learning (artificial intelligence), Metrics, object detection, pattern clustering, pubcrawl, Regularization, resilience, Resiliency, Scalability, security of data, social networking (online), video quality, video signal processing

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.

Citation Keyzhu_deepfake_2020