Summary of Shaking the Fake: Detecting Deepfake Videos in Real Time Via Active Probes, by Zhixin Xie et al.
Shaking the Fake: Detecting Deepfake Videos in Real Time via Active Probes
by Zhixin Xie, Jun Luo
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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 A novel real-time deepfake detection method is proposed to counter the malicious use of generative AI in producing fake videos. The existing works on deepfake detection rely on learning passive features that may perform poorly beyond seen datasets. SFake innovatively exploits deepfake models’ inability to adapt to physical interference by actively sending probes to trigger mechanical vibrations on a smartphone, resulting in controllable features on the footage. The method determines whether the face is swapped by deepfake based on the consistency of the facial area with the probe pattern. In this paper, SFake is implemented, evaluated on a self-built dataset, and compared with six other detection methods. The results show that SFake outperforms other detection methods in terms of detection accuracy, process speed, and memory consumption. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deepfake technology can create fake videos by swapping faces or objects. This has been used to spread misinformation and even commit financial scams. To stop this, researchers have developed ways to detect deepfakes. One problem is that these methods don’t work well when they’re not trained on the same type of data as the deepfake being detected. A new method called SFake tries to solve this by adding some “noise” or vibrations to the video while it’s being recorded. This makes it harder for the fake face swap to be consistent, and SFake can use this inconsistency to detect that a video is fake. The researchers tested SFake on their own data and found that it was better than other methods at detecting deepfakes. |
