Swin Transformer for Robust Differentiation of Real and Synthetic Images: Intra- and Inter-Dataset Analysis

TL;DR

This paper introduces a Swin Transformer-based model that effectively distinguishes CGI from real images with high accuracy, demonstrating robustness across multiple datasets and generalization to different domains.

Contribution

The study presents a novel application of Swin Transformer architecture for CGI detection, achieving high accuracy and robustness in intra- and inter-dataset evaluations.

Findings

Achieved 97-99% accuracy across datasets

Demonstrated strong domain generalization

Validated robustness in intra- and inter-dataset tests

Abstract

\textbf{Purpose} This study aims to address the growing challenge of distinguishing computer-generated imagery (CGI) from authentic digital images in the RGB color space. Given the limitations of existing classification methods in handling the complexity and variability of CGI, this research proposes a Swin Transformer-based model for accurate differentiation between natural and synthetic images. \textbf{Methods} The proposed model leverages the Swin Transformer's hierarchical architecture to capture local and global features crucial for distinguishing CGI from natural images. The model's performance was evaluated through intra-dataset and inter-dataset testing across three distinct datasets: CiFAKE, JSSSTU, and Columbia. The datasets were tested individually (D1, D2, D3) and in combination (D1+D2+D3) to assess the model's robustness and domain generalization capabilities. \textbf{Results} The Swin Transformer-based model demonstrated high accuracy, consistently achieving a range of 97-99\% across all datasets and testing scenarios. These results confirm the model's effectiveness in detecting CGI, showcasing its robustness and reliability in both intra-dataset and inter-dataset evaluations. \textbf{Conclusion} The findings of this study highlight the Swin Transformer model's potential as an advanced tool for digital image forensics, particularly in distinguishing CGI from natural images. The model's strong performance across multiple datasets indicates its capability for domain generalization, making it a valuable asset in scenarios requiring precise and reliable image classification.