Summary of Real, Fake and Synthetic Faces — Does the Coin Have Three Sides?, by Shahzeb Naeem et al.
Real, fake and synthetic faces – does the coin have three sides?
by Shahzeb Naeem, Ramzi Al-Sharawi, Muhammad Riyyan Khan, Usman Tariq, Abhinav Dhall, Hasan Al-Nashash
First submitted to arxiv on: 2 Apr 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 proposed research explores trends and patterns in real, deepfake, and synthetic facial images using eight deep learning models. The study analyzes the performance of these models in distinguishing between the three image categories and investigates their image properties at both global and local levels. The results show that the ViT Patch-16 model excels in detecting synthetic facial images with high sensitivity, specificity, precision, and accuracy. The analysis reveals noticeable differences across the three image categories, which can inform the development of better algorithms for facial image generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research investigates how to tell real, fake, and artificial facial images apart. It uses powerful computer models called deep learning models to see how well they can do this job. The study finds that one model is very good at spotting synthetic (artificial) faces, and it does better than the others at this task. By looking at both the whole image and specific parts of the image, the researchers discover some interesting patterns. This information can help create new ways to generate facial images and shows that real, fake, and artificial faces are distinct. |
Keywords
» Artificial intelligence » Deep learning » Image generation » Precision » Vit
