Summary of A Large-scale Universal Evaluation Benchmark For Face Forgery Detection, by Yijun Bei et al.
A Large-scale Universal Evaluation Benchmark For Face Forgery Detection
by Yijun Bei, Hengrui Lou, Jinsong Geng, Erteng Liu, Lechao Cheng, Jie Song, Mingli Song, Zunlei Feng
First submitted to arxiv on: 13 Jun 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 A newly constructed benchmark, called DeepFaceGen, is designed to evaluate the effectiveness and generalizability of face forgery detection techniques. This large-scale evaluation dataset consists of 776,990 real face images/videos and 773,812 fake facial content samples generated using 34 mainstream face generation techniques. The DeepFaceGen benchmark aims to quantitatively assess the performance of various face forgery detection methods from different perspectives. An analysis of the 13 mainstream face forgery detection techniques was conducted, yielding significant findings that suggest potential directions for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Fake facial images and videos can be easily created using AI-generated content technology, which has led to a surge in proposed face forgery detection techniques. However, it’s crucial to evaluate these methods’ effectiveness and generalizability. To address this, the DeepFaceGen benchmark was developed, comprising real and fake facial content samples generated using various face generation techniques. The goal is to quantitatively assess the performance of different face forgery detection methods. |
