Khái niệm cốt lõi
The author argues that utilizing style latent flows can enhance deepfake video detection by capturing temporal variations in facial attributes, leading to improved generalization across different generative models.
Tóm tắt
This paper introduces a novel approach for deepfake detection using style latent vectors to capture temporal changes in facial expressions and geometric transformations. By leveraging supervised contrastive learning and a style attention module, the model demonstrates superior performance in cross-dataset scenarios. The study highlights the importance of considering temporal changes in style latent vectors for robust deepfake detection.
Thống kê
We noticed that the level-wise differences vary across deep-fake domains, but the variance of style latent vectors is particularly lower in certain levels of the style latent vectors for fake videos than in real videos.
Our results demonstrate that deep-fake videos have a distinct variance in style flow compared to real videos.
The performance on the FSh dataset also reaches a top-2 level, with the highest average score, indicating that our model is a deep-fake detection algorithm with generalization capability.
The performance under specific perturbation conditions exhibits a slight deficiency, which is attributed to the fact that the pSp encoder used for extracting style latent vectors was created without considering noise.
Trích dẫn
"We propose a novel video deepfake detection framework that is based on the unnatural variation of the style latent vectors."
"Our approach demonstrates state-of-the-art performance in various deep-fake detection scenarios, including cross-dataset and cross-manipulation settings."
"The contributions of our work are summarized as follows."