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

arXiv: https://arxiv.org/abs/2409.04742 · HTML: https://mlsanity.com/paper/2409.04742/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 7 Sep 2024.

## Builds on (most cited first, 2 of 2)

- [Exposing Computer Generated Images by Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1711.10394/) (2017)
- [Domain Generalized Recaptured Screen Image Identification Using SWIN Transformer](https://mlsanity.com/paper/2407.17170/) (2024)

## Built on by (most cited first, 1 of 1)

- [Data Safety: Synthetic Data Quality Analysis Using CIFAKE Dataset](https://mlsanity.com/paper/2607.12165/) (2026)

## Abstract (arXiv)

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.