Summary of Deepfake Media Generation and Detection in the Generative Ai Era: a Survey and Outlook, by Florinel-alin Croitoru et al.
Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook
by Florinel-Alin Croitoru, Andrei-Iulian Hiji, Vlad Hondru, Nicolae Catalin Ristea, Paul Irofti, Marius Popescu, Cristian Rusu, Radu Tudor Ionescu, Fahad Shahbaz Khan, Mubarak Shah
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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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 The paper surveys recent advancements in deepfake generation and detection techniques, including diffusion models and Neural Radiance Fields. It covers all types of deepfakes (image, video, audio, and multimodal) and identifies various procedures used to alter or generate fake content. The authors construct a taxonomy of deepfake generation and detection methods, illustrating important groups and domains where these methods are applied. They gather datasets for deepfake detection, provide rankings of the best-performing detectors on popular datasets, and develop a novel multimodal benchmark to evaluate detectors on out-of-distribution content. The results show that state-of-the-art detectors fail to generalize to unseen deepfake generators. Finally, the paper proposes future directions for obtaining robust and powerful deepfake detectors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about fake videos and images that are getting really good at fooling people. These “deepfakes” can be used to spread false information or make someone look like they’re doing something they’re not. The researchers looked at what’s being done in this field and how well the methods work. They found that some methods are better than others, but none of them can detect all deepfakes. So, they developed a new way to test these detectors and found that even the best ones can be tricked. The paper says we need to keep working on making better detectors. |
Keywords
» Artificial intelligence » Diffusion
