Summary of Disrupting Diffusion-based Inpainters with Semantic Digression, by Geonho Son et al.
Disrupting Diffusion-based Inpainters with Semantic Digression
by Geonho Son, Juhun Lee, Simon S. Woo
First submitted to arxiv on: 14 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 This research proposes a novel framework called DDD (Digression guided Diffusion Disruption) to combat the spread of malicious deepfakes on the web and social media. The increasing prevalence of visual misinformation has been fueled by foundational text-to-image diffusion models, such as Stable Diffusion inpainters. These models can generate fake images of individuals, private figures, or copyrighted content with alarming accuracy. To counteract this issue, DDD adds adversarial noise to the context image, disrupting the inpainting synthesis process. The framework achieves stronger disruption and a higher success rate than previous approaches while reducing GPU memory requirements and speeding up optimization by three times. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is being developed to stop fake images from spreading online. These fake images are called deepfakes, and they can make it seem like someone is doing something when really they’re not. This is a big problem because it’s hard to tell what’s real and what’s not. The new method uses special noise to disrupt the way these fake images are made. It makes it harder for them to be created in the first place, and it does this without needing a lot of powerful computers. |
Keywords
» Artificial intelligence » Diffusion » Optimization
