Optimizing the Baseline Approach for the 2024 ACM Multimedia Grand Challenge in Artificial Intelligence Generated Image Detection

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.

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Publication Details

Published
2024-10-26
DOI
https://doi.org/10.1145/3664647.3689331
Primary Topic
Generative Adversarial Networks and Image Synthesis
Type
article
Field-Weighted Citation Impact
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article

Optimizing the Baseline Approach for the 2024 ACM Multimedia Grand Challenge in Artificial Intelligence Generated Image Detection

Jin Chen
Generative Adversarial Networks and Image Synthesis
article

Optimizing the Baseline Approach for the 2024 ACM Multimedia Grand Challenge in Artificial Intelligence Generated Image Detection

Jin Chen
article en

Abstract

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.

China Central Television (CN)
Openalex Percentile: Top 82%
Generative Adversarial Networks and Image Synthesis
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