Summary of Herd Mentality in Augmentation — Not a Good Idea! a Robust Multi-stage Approach Towards Deepfake Detection, by Monu et al.
Herd Mentality in Augmentation – Not a Good Idea! A Robust Multi-stage Approach towards Deepfake Detection
by Monu, Rohan Raju Dhanakshirur
First submitted to arxiv on: 7 Oct 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 The rapid increase in deepfake technology has raised concerns about digital media integrity. Detecting deepfakes is crucial for safeguarding digital media, but most standard image classifiers struggle to distinguish between fake and real faces. Our analysis reveals that this failure is due to the model’s inability to explicitly focus on artefacts typically present in deepfakes. We propose an enhanced architecture based on the GenConViT model, incorporating weighted loss and update augmentation techniques, as well as masked eye pretraining. This proposed model improves the F1 score by 1.71% and accuracy by 4.34% on the Celeb-DF v2 dataset. Our approach demonstrates a significant improvement in detecting deepfakes, showcasing its potential for real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deepfake technology has become more common, making it harder to tell what’s real and what’s fake online. To stop this from getting out of hand, we need better ways to detect deepfakes. Right now, most computer programs struggle to tell the difference between a fake face and a real one. We figured out why they’re failing and came up with a new way to make them work better. Our method is based on an existing model called GenConViT and makes some adjustments to help it focus on the telltale signs of deepfakes. It works! We tested it on a big dataset and saw a 4% improvement in accuracy. |
Keywords
» Artificial intelligence » F1 score » Pretraining
