Year: 2024
Type: article
Abstract: With the advancement of technology, artificial intelligence-generated content (AIGC) has facilitated people's lives while also giving rise to numerous issues. Traditional AIGC detection methods have suffered from low accuracy and other problems, rendering them ineffective in detecting AI-generated images. Meanwhile, models trained on large datasets are constrained by the dataset size. Recent research has demonstrated that although training-free models are efficacious, their generalization ability poses a problem. In this paper, we propose a model based on capsule neural networks. The capsule network model acquires the spatial features of fake images and outputs image classification results via softmax classifier. We trained and evaluated the proposed AIGC image detection model using the publicly available MINIST dataset. The experimental results indicate that the capsule network-based model surpasses many traditional AIGC image detection models.
Author: Jiarui Zhu
Institution: Wuxi University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: American Indian Graduate Center
OpenAlex ID: W4405696645