Summary of Sok: Systematization and Benchmarking Of Deepfake Detectors in a Unified Framework, by Binh M. Le et al.
SoK: Systematization and Benchmarking of Deepfake Detectors in a Unified Framework
by Binh M. Le, Jiwon Kim, Simon S. Woo, Kristen Moore, Alsharif Abuadbba, Shahroz Tariq
First submitted to arxiv on: 9 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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 Deep learning-based deepfake detection systems have become increasingly important in recent years due to the widespread creation and dissemination of deepfakes. Existing detectors often rely on lab-generated datasets for validation, which may not prepare them for novel, real-world scenarios. This paper reviews and analyzes state-of-the-art deepfake detectors against several critical criteria, including their efficacy, generalizability, and robustness to different types of attacks. The authors propose a unified conceptual framework that categorizes detectors into 4 high-level groups and 13 fine-grained sub-groups based on their performance across various attack scenarios. This systematized analysis provides insights into the factors affecting detector efficacy and paves the way for future research and the development of more proactive defenses against deepfakes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deepfakes are fake videos or images that can be very convincing and misleading. Researchers have been working on ways to detect deepfakes, but many detectors only work well with certain types of fake media. This paper looks at 16 different deepfake detection systems and sees how they do in different situations. The authors group the detectors into categories based on their performance and show which ones are good at detecting different kinds of deepfakes. |
Keywords
* Artificial intelligence * Deep learning
