Detecting Deepfakes with Metric Learning
This addresses the challenge of verifying digital media authenticity on social media platforms where compression is common, though it appears incremental as it builds on existing deep learning methods.
The paper tackles the problem of detecting deepfake videos in high-compression scenarios, such as on social media, by proposing a metric learning approach using a triplet network. It achieves state-of-the-art results, including a 99.2% AUC on the Celeb-DF dataset and 90.71% accuracy on a highly compressed Neural Texture dataset.
With the arrival of several face-swapping applications such as FaceApp, SnapChat, MixBooth, FaceBlender and many more, the authenticity of digital media content is hanging on a very loose thread. On social media platforms, videos are widely circulated often at a high compression factor. In this work, we analyze several deep learning approaches in the context of deepfakes classification in high compression scenario and demonstrate that a proposed approach based on metric learning can be very effective in performing such a classification. Using less number of frames per video to assess its realism, the metric learning approach using a triplet network architecture proves to be fruitful. It learns to enhance the feature space distance between the cluster of real and fake videos embedding vectors. We validated our approaches on two datasets to analyze the behavior in different environments. We achieved a state-of-the-art AUC score of 99.2% on the Celeb-DF dataset and accuracy of 90.71% on a highly compressed Neural Texture dataset. Our approach is especially helpful on social media platforms where data compression is inevitable.