Summary of Nearly Solved? Robust Deepfake Detection Requires More Than Visual Forensics, by Guy Levy and Nathan Liebmann
Nearly Solved? Robust Deepfake Detection Requires More than Visual Forensics
by Guy Levy, Nathan Liebmann
First submitted to arxiv on: 7 Dec 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 Deepfake detection is crucial to prevent social engineering attacks, which are becoming increasingly sophisticated. Recent state-of-the-art detectors have been shown to be vulnerable to classical adversarial attacks in a realistic black-box setting, casting doubt on their usability. Our research argues that deepfakes’ higher semantics hold the key to robust features and presents evidence that a detector based on a semantic embedding model is less susceptible to black-box perturbation attacks. We also demonstrate that large visuo-lingual models like GPT-4o can perform zero-shot deepfake detection better than current state-of-the-art methods, while introducing a novel attack based on high-level semantic manipulation. Furthermore, we propose hybridizing low- and high-level detectors to improve adversarial robustness by leveraging their complementary strengths and weaknesses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deepfakes are fake videos that can trick people into believing what they see isn’t real. Detecting them is important to prevent harmful attacks. Our research shows that the best deepfake detectors right now aren’t good enough because they’re vulnerable to being tricked themselves. We think the key to making better detectors lies in understanding the deeper meaning behind fake videos. Our results show that using a special type of model called GPT-4o can be more effective at detecting deepfakes than current methods, and we also introduce a new way to make fake videos even harder to detect. |
Keywords
» Artificial intelligence » Embedding » Gpt » Semantics » Zero shot
