Efficient Image Classification Using ReXNet: Distinguishing AI- Generated Images from Real Ones

Yanxi Liu

2024

Abstract

Image classification, a topic of growing interest in recent years, holds significant applications in computer vision and medical domains. This paper introduces an image classifier built on the Rank eXpandsion Networks (ReXNet) model, specifically designed to effectively distinguish between Artificial Intelligence (AI)-generated and real images. Leveraging Convolutional Neural Network (CNN) architecture, this method utilizes deep separable convolution layers to minimize parameters and computational complexity. It also features a compact network structure and optimized hyperparameters for efficient feature extraction and classification. Experimental results demonstrate the model's high classification accuracy across various image types, showcasing its efficiency. This experiment underscores the ReXNet model's potential in image classification and offers valuable insights for future research directions. This study not only validates the accuracy and generalization capabilities of lightweight models but also lays a solid groundwork for more intricate image classification studies. The findings highlight the importance of efficient model design in addressing real-world image classification challenges, particularly in distinguishing between AI-generated and authentic images, with implications for advancing both theoretical understanding and practical applications in the field.

Paper Citation

in Harvard Style

Liu Y. (2024). Efficient Image Classification Using ReXNet: Distinguishing AI- Generated Images from Real Ones. In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-713-9, SciTePress, pages 234-238. DOI: 10.5220/0012925200004508

in Bibtex Style

@conference{emiti24,
author={Yanxi Liu},
title={Efficient Image Classification Using ReXNet: Distinguishing AI- Generated Images from Real Ones},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={234-238},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012925200004508},
isbn={978-989-758-713-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - Efficient Image Classification Using ReXNet: Distinguishing AI- Generated Images from Real Ones
SN - 978-989-758-713-9
AU - Liu Y.
PY - 2024
SP - 234
EP - 238
DO - 10.5220/0012925200004508
PB - SciTePress