Summary of Sida: Social Media Image Deepfake Detection, Localization and Explanation with Large Multimodal Model, by Zhenglin Huang et al.
SIDA: Social Media Image Deepfake Detection, Localization and Explanation with Large Multimodal Model
by Zhenglin Huang, Jinwei Hu, Xiangtai Li, Yiwei He, Xingyu Zhao, Bei Peng, Baoyuan Wu, Xiaowei Huang, Guangliang Cheng
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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 The paper addresses the risks posed by generative models in creating highly realistic images, which can spread misinformation and erode trust in digital content. To combat this issue, researchers have yet to develop a comprehensive deepfake detection dataset for social media or an effective solution. The authors introduce SID-Set, a large and diversified dataset featuring 300K AI-generated/tampered and authentic images with annotations. They also propose SIDA, an image deepfake detection, localization, and explanation framework that leverages large multimodal models to discern authenticity, localize tampered regions, and provide textual explanations. Compared to state-of-the-art models on SID-Set and other benchmarks, SIDA achieves superior performance across diversified settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about stopping fake images from spreading misinformation online. Right now, there are no good ways to detect when an image has been changed or created using AI. To fix this, the researchers created a big dataset with lots of different AI-generated and real images. They also made a new tool called SIDA that can tell if an image is real or fake, find what parts have been changed, and explain why it thinks something is fake or real. This tool works better than other tools on big datasets. |
