Summary of Towards General Deepfake Detection with Dynamic Curriculum, by Wentang Song et al.
Towards General Deepfake Detection with Dynamic Curriculum
by Wentang Song, Yuzhen Lin, Bin Li
First submitted to arxiv on: 15 Oct 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 This paper proposes a novel approach to improve the performance of deepfake detection models by incorporating sample hardness into their training process. The authors introduce Dynamic Facial Forensic Curriculum (DFFC), which leverages curriculum learning to make the model focus on increasingly challenging samples during training. This is achieved through the integration of facial quality scores and instantaneous instance loss, resulting in a dynamic measure of sample hardness called Dynamic Forensic Hardness (DFH). The pacing function controls the data subsets from easy to hard throughout the training process based on DFH. Experimental results demonstrate that DFFC can enhance both within- and cross-dataset performance of various end-to-end deepfake detectors through a plug-and-play approach, indicating its potential to help models learn general forgery discriminative features by exploiting information from hard samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about making computer programs better at detecting fake pictures or videos. Usually, these programs are trained to spot specific things in the picture or video that have been changed. However, they often miss important clues because they’re not looking at the quality of the image as a whole. The authors developed a new way to train these programs by showing them easier and harder examples of fake pictures or videos throughout the training process. This helps the program learn how to spot fake images more effectively. The results show that this new approach can help different programs detect fake images better, which is important for keeping people safe online. |
Keywords
» Artificial intelligence » Curriculum learning
