Summary of Forgerysleuth: Empowering Multimodal Large Language Models For Image Manipulation Detection, by Zhihao Sun et al.
ForgerySleuth: Empowering Multimodal Large Language Models for Image Manipulation Detection
by Zhihao Sun, Haoran Jiang, Haoran Chen, Yixin Cao, Xipeng Qiu, Zuxuan Wu, Yu-Gang Jiang
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 Multimodal large language models (M-LLMs) have opened doors for various multimodal tasks. However, their potential in image manipulation detection remains unexplored. When directly applied to the image manipulation detection (IMD) task, M-LLMs often produce reasoning texts that suffer from hallucinations and overthinking. To address this, researchers propose ForgerySleuth, which leverages M-LLMs to perform comprehensive clue fusion and generate segmentation outputs indicating specific regions that are tampered with. The team constructs the ForgeryAnalysis dataset through the Chain-of-Clues prompt, including analysis and reasoning text to upgrade the image manipulation detection task. A data engine is also introduced to build a larger-scale dataset for the pre-training phase. Experimental results demonstrate the effectiveness of ForgeryAnalysis and show that ForgerySleuth significantly outperforms existing methods in generalization, robustness, and explainability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to detect when someone has edited or manipulated an image. This is a hard problem for computers, but researchers have come up with a new approach called ForgerySleuth. They use special AI models to help identify specific parts of the image that have been changed. The team also created a dataset filled with images and explanations about what’s real and what’s fake. By testing their approach on this dataset, they found that it worked better than other methods in making sure the results were accurate and easy to understand. |
Keywords
» Artificial intelligence » Generalization » Prompt
