מושגי ליבה
Multimodal LLMs show promise in detecting AI-generated images, offering a user-friendly alternative to traditional methods.
תקציר
The study explores the use of multimodal Large Language Models (LLMs) for DeepFake detection. It investigates the effectiveness of prompts and the performance of GPT4V and Gemini 1.0 Pro models. The research highlights the importance of well-crafted prompts and interactive strategies to improve detection accuracy. Results indicate that multimodal LLMs can distinguish between real and AI-generated images, but further refinement is needed for enhanced performance.
- Introduction to Generative AI models and DeepFakes.
- Current methods for DeepFake detection using machine learning algorithms.
- Role of Large Language Models (LLMs) like ChatGPT in media forensics.
- Experiment methodology using GPT4V model for DeepFake detection.
- Qualitative and quantitative results comparing GPT4V and Gemini 1.0 Pro models.
- Ablation studies on different text prompts' impact on detection performance.
- Potential improvements through decomposition-based prompting and few-shot prompting techniques.
- Conclusion on leveraging multimodal LLMs for media forensics with future directions.
סטטיסטיקה
"Multimodal LLMs demonstrate a certain capability to distinguish between authentic and AI-generated imagery, drawing on their semantic understanding."
"The efficacy of multimodal LLMs in identifying AI-generated images is satisfactory, with an Area Under the Curve (AUC) score of approximately 75%."
"Presently, multimodal LLMs do not incorporate signal cues or data-driven approaches for this task."
ציטוטים
"We hope that this study will encourage future exploration of the use and improvement of LLMs for media forensics and DeepFake detection."