Summary of Exploiting Style Latent Flows For Generalizing Deepfake Video Detection, by Jongwook Choi et al.
Exploiting Style Latent Flows for Generalizing Deepfake Video Detection
by Jongwook Choi, Taehoon Kim, Yonghyun Jeong, Seungryul Baek, Jongwon Choi
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach for detecting fake videos leverages the analysis of style latent vectors’ abnormal behavior in temporal changes during generated videos. The framework utilizes a StyleGRU module, trained via contrastive learning, to represent dynamic style latent vector properties. Additionally, a style attention module integrates these features with content-based ones, enabling the detection of visual and temporal artifacts. Experimental results demonstrate the approach’s superiority in cross-dataset and cross-manipulation scenarios for deepfake video detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to spot fake videos by looking at how they change over time. The generated videos often have unusual patterns that can be detected using special computer models. These models are trained to find changes in the style of the video, like if someone’s face is being manipulated. The approach uses two key components: one that tracks these changes and another that focuses on specific parts of the video. By combining these components, the method outperforms existing methods in detecting fake videos. |
Keywords
» Artificial intelligence » Attention
