Enhancing Image Authenticity Detection: Swin Transformers and Color Frame Analysis for CGI vs. Real Images

TL;DR

This paper introduces a novel method combining Swin Transformers and color frame analysis to improve the accuracy, speed, and robustness of distinguishing CGI images from real photographs, addressing challenges posed by highly realistic CGI.

Contribution

It presents a new approach that leverages Swin Transformers and color frame preprocessing to outperform existing methods in image authenticity detection without handcrafted features.

Findings

Achieves state-of-the-art accuracy in CGI vs. real image classification.

Demonstrates improved processing speed and robustness against image manipulations.

Effectively detects highly realistic CGI images in various manipulated conditions.

Abstract

The rapid advancements in computer graphics have greatly enhanced the quality of computer-generated images (CGI), making them increasingly indistinguishable from authentic images captured by digital cameras (ADI). This indistinguishability poses significant challenges, especially in an era of widespread misinformation and digitally fabricated content. This research proposes a novel approach to classify CGI and ADI using Swin Transformers and preprocessing techniques involving RGB and CbCrY color frame analysis. By harnessing the capabilities of Swin Transformers, our method foregoes handcrafted features instead of relying on raw pixel data for model training. This approach achieves state-of-the-art accuracy while offering substantial improvements in processing speed and robustness against joint image manipulations such as noise addition, blurring, and JPEG compression. Our findings highlight the potential of Swin Transformers combined with advanced color frame analysis for effective and efficient image authenticity detection.