Optimizing the Baseline Approach for the 2024 ACM Multimedia Grand Challenge in Artificial Intelligence Generated Image Detection
Jin Chen · 2024
This paper presents an optimized approach for the AI-generated image detection task in the 2024 ACM Multimedia Grand Challenge. Given the rapidly evolving capabilities of generative models, traditional detection methods often struggle with accuracy and generalization. The proposed solution builds upon the baseline by integrating advanced model architecture enhancements and novel detection modules, specifically designed to address the complexities of AI-generated images. Through rigorous testing, the approach demonstrates significant improvements in precision, particularly in identifying images produced by unknown generative models. This work highlights the critical need for adaptable and robust detection methods to keep pace with the advancements in AI-generated content and sets a new standard for future research and development in this domain.