Efficient Temporally-Aware DeepFake Detection using H.264 Motion Vectors

arXiv:2311.10788v2 Announce Type: replace
Abstract: Video DeepFakes are fake media created with Deep Learning (DL) that manipulate a person's expression or identity. Most current DeepFake detection methods analyze each frame independently, ignoring inconsistencies and unnatural movements between frames. Some newer methods employ optical flow models to capture this temporal aspect, but they are computationally expensive. In contrast, we propose using the related but often ignored Motion Vectors (MVs) and Information Masks (IMs) from the H.264 video codec, to detect temporal …

abstract analyze arxiv cs.ai cs.cv current deepfake deepfakes deep learning detection detection methods fake flow identity media movements optical optical flow person temporal type vectors video