Detection of AI-Generated Images From Various Generators Using Gated Expert Convolutional Neural Network

The rapid advancement of artificial intelligence (AI), particularly in text-to-image generative models, has led to a proliferation of synthetic images. This progress, while remarkable, raises concerns about misuse in fraudulent activities. To address this issue, we propose a Convolutional Neural Network (CNN)-based approach for classifying AI-generated images from multiple generators. We introduce a gated CNN model that leverages mixed datasets for improved training efficiency and performance. This approach eliminates the need for extensive tuning with each new dataset and mitigates the risk of catastrophic forgetting. Our experiments demonstrate that the gated CNN model slightly outperforms traditional single CNN models, providing a more robust solution for identifying AI-generated images. This paper presents a comprehensive comparison of methods and offers insights into enhancing the classification of AI-generated images.