Year: 2025
Type: article
Abstract: Increasingly, images generated by artificial intelligence (AI) are being used within interior design as a source of authenticity and ethical use. Based on limited Convolutional Neural Network (CNN) capabilities in data descriptive processes, including long-range dependencies and global patterns, this study examines how Vision Transformer (ViT) can be utilized in detecting AI-generated interior design images. We finetuned and evaluated four ViT models, ViT-B16, ViT-B32, ViT-L16, and ViT-L32, on 1,000 samples per class dataset. Accuracy, precision, recall, F1-score, and computational efficiency were used to assess performance. Results show that models with smaller patch sizes (i.e., 16×16) perform better than larger ones (i.e., 32×32). It was found that ViT-B16 and ViT-L16 had the highest accuracy (96.25%) and F1- score (0.9625) in identifying minor inconsistencies in the AI-generated images. ViT-B32 and ViT-L32 enjoy better computational efficiency based on lower classification performance (80.00% and 81.25% accuracy, respectively, for ViT-B32 and ViT-L32). The best tradeoff between accuracy and resource efficiency turns out to be ViT-B16. However, computational costs were higher with ViT — ViT-L16, although just as accurate. Computationally, ViT-B32 and ViT-L32 were also efficient, which was more appropriate for realtime applications with lower accuracy than speed. Through this work, we contribute a domain-specific deep learning framework for AI-generated image detection in interior design to increase authenticity verification. Future work will address improving computational efficiency and generalizing the model across all (or most) generative models and design styles.
Source: Informatica
Author: Hui Wang
Institution: Anhui Business Vocational College
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409032835