Summary of A Quality-centric Framework For Generic Deepfake Detection, by Wentang Song et al.
A Quality-Centric Framework for Generic Deepfake Detection
by Wentang Song, Zhiyuan Yan, Yuzhen Lin, Taiping Yao, Changsheng Chen, Shen Chen, Yandan Zhao, Shouhong Ding, Bin Li
First submitted to arxiv on: 8 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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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 quality-centric framework for deepfake detection that tackles the generalization issue by harnessing forgery quality in training data. The existing methods often train detectors on a mix of deepfakes with varying forgery qualities, which can lead to shortcuts and hurt performance. The proposed framework consists of a Quality Evaluator, a low-quality data enhancement module, and a learning pacing strategy that incorporates forgery quality into the training process, inspired by curriculum learning. The framework assesses forgery quality using both static and dynamic methods, combining their scores to produce a final rating for each training sample. This rating guides the selection of deepfake samples for training, with higher-rated samples having a higher probability of being chosen. Additionally, the paper introduces a novel frequency data augmentation method specifically designed for low-quality forgery samples, which helps to reduce obvious forgery traces and improve their realism. The proposed method can be applied in a plug-and-play manner and significantly enhances generalization performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem in detecting fake videos by making the training process more realistic. Right now, most methods train on a mix of fake videos with different levels of quality, which means they can easily spot the fake parts instead of learning to detect the real ones. The new method is like a curriculum for deepfake detectors, starting with easy-to-spot fake videos and gradually moving to more realistic ones. It also has a special way of making low-quality fake videos look more natural, which helps the detector learn to recognize them better. Overall, this method can be easily used with existing systems and makes them better at detecting deepfakes. |
Keywords
» Artificial intelligence » Curriculum learning » Data augmentation » Generalization » Probability
