Summary of Unveiling the Truth: Exploring Human Gaze Patterns in Fake Images, by Giuseppe Cartella et al.
Unveiling the Truth: Exploring Human Gaze Patterns in Fake Images
by Giuseppe Cartella, Vittorio Cuculo, Marcella Cornia, Rita Cucchiara
First submitted to arxiv on: 13 Mar 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 approach leverages human semantic knowledge to improve fake image detection by collecting a novel dataset of partially manipulated images generated using diffusion models. An eye-tracking experiment is conducted to record observers’ eye movements while viewing real and fake stimuli, revealing distinct patterns in how humans perceive genuine and altered images. The study shows that when perceiving counterfeit samples, humans tend to focus on more confined regions, whereas the observational pattern for genuine images is more dispersed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generative models can create high-quality and realistic images with just a natural language description of what you want to see. But this also raises concerns about spreading misinformation and malicious content. Researchers are working on detecting fake images by looking at low-level features or fingerprints left by the generative model during image creation. This study uses human knowledge to improve fake detection frameworks, collecting a dataset of partially manipulated images generated using diffusion models. It also does an eye-tracking experiment to see how people look at real and fake images. |
Keywords
* Artificial intelligence * Diffusion * Generative model * Tracking
