Summary of Explicit Correlation Learning For Generalizable Cross-modal Deepfake Detection, by Cai Yu et al.
Explicit Correlation Learning for Generalizable Cross-Modal Deepfake Detection
by Cai Yu, Shan Jia, Xiaomeng Fu, Jin Liu, Jiahe Tian, Jiao Dai, Xi Wang, Siwei Lyu, Jizhong Han
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 presents a novel approach to detecting deepfakes that generalizes well across various types of deepfakes, including those generated using different modalities. The proposed method, which involves learning cross-modal correlations through a correlation distillation task, aims to prevent the model from overfitting to specific audio-visual synchronizations. To evaluate this approach, the authors create the Cross-Modal Deepfake Dataset (CMDFD), comprising four generation methods for detecting diverse deepfakes. Experimental results on CMDFD and FakeAVCeleb datasets show that the proposed method outperforms existing state-of-the-art approaches in terms of generalizability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deepfakes are fake videos or images that can be very convincing, but they’re a problem because they can spread misinformation. To stop deepfakes from spreading lies, researchers need to develop better ways to detect them. This paper helps with that by creating a new way to detect deepfakes that works well even when the fake is created using different methods or formats (like audio and video). The approach uses something called correlation distillation to learn how different parts of an image or video relate to each other, which makes it better at detecting deepfakes. To test this approach, the researchers created a big dataset of deepfakes with different generation methods. Their results show that their method is better than others at detecting deepfakes. |
Keywords
* Artificial intelligence * Distillation * Overfitting
