2024/09/07 by Preeti Mehta, Mehta, Preeti, Aman Sagar +3
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #Currency Recognition and Detection #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.2409.04742
openalex publication_date 2024/09/07 · openalex created_date 2024/10/22 · openalex updated_date 2026/07/28
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.