Video Transformer for Deepfake Detection with Incremental Learning

By Sohail Ahmed Khan, Hang Dai. 2021

91 citations recorded by the source corpus.

Face forgery by deepfake is widely spread over the internet and this raises severe societal concerns. In this paper, we propose a novel video transformer with incremental learning for detecting deepfake videos. To better align the input face images, we use a 3D face reconstruction method to generate UV texture from a single input face image. The aligned face image can also provide pose, eyes blink and mouth movement information that cannot be perceived in the UV texture image, so we use both face images and their UV texture maps to extract the image features. We present an incremental learning strategy to fine-tune the proposed model on a smaller amount of data and achieve better deepfake detection performance. The comprehensive experiments on various public deepfake datasets demonstrate that the proposed video transformer model with incremental learning achieves state-of-the-art performance in the deepfake video detection task with enhanced feature learning from the sequenced data.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

1. Zhiwu Huang
2. Yabin Wang
3. Xiaopeng Hong
4. Han-Jia Ye
5. De-chuan Zhan
6. Da-Wei Zhou
7. Wu-Jun Li
8. Yan-Shuo Liang

Citations in the corpus

• S-Prompts Learning with Pre-trained Transformers: An Occam's Razor for Domain Incremental Learning (2022, methodology): “Recently, some of work like [48, 31, 29, 36, 66, 18, 78] adapts the CIL methods to the DIL problems such as continual deepfake detection, where each domain distinguishes between deepfakes and reals.”