Summary of Ffaa: Multimodal Large Language Model Based Explainable Open-world Face Forgery Analysis Assistant, by Zhengchao Huang et al.
FFAA: Multimodal Large Language Model based Explainable Open-World Face Forgery Analysis Assistant
by Zhengchao Huang, Bin Xia, Zicheng Lin, Zhun Mou, Wenming Yang, Jiaya Jia
First submitted to arxiv on: 19 Aug 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 rapid advancement of deepfake technologies has sparked widespread public concern regarding face forgery and its potential threats to public information security. The unknown and diverse forgery techniques, varied facial features, and complex environmental factors pose significant challenges for face forgery analysis. Existing datasets lack descriptive annotations of these aspects, making it difficult for models to distinguish between real and forged faces using only visual information amid various confounding factors. To address this challenge, a novel Open-World Face Forgery Analysis VQA (OW-FFA-VQA) task is introduced along with its corresponding benchmark. A dataset featuring a diverse collection of real and forged face images with essential descriptions and reliable forgery reasoning is established based on which FFAA: Face Forgery Analysis Assistant is introduced. This consists of a fine-tuned Multimodal Large Language Model (MLLM) and Multi-answer Intelligent Decision System (MIDS). The impact of fuzzy classification boundaries is effectively mitigated by integrating hypothetical prompts with MIDS, enhancing model robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deepfake technologies have raised concerns about face forgery threatening public information security. Face forgery analysis faces challenges due to unknown techniques, varied facial features, and environmental factors. Existing datasets lack annotations making it hard for models to distinguish between real and forged faces. To address this, a new task (OW-FFA-VQA) and benchmark are introduced along with a dataset featuring real and forged face images with descriptions. A model called FFAA is developed using Multimodal Large Language Model and Multi-answer Intelligent Decision System. This helps improve accuracy and robustness. |
Keywords
» Artificial intelligence » Classification » Large language model
