Enhancing Image Authenticity Detection: Swin Transformers and Color Frame Analysis for CGI vs. Real Images
This addresses the challenge of detecting misinformation from fabricated images, though it appears incremental as it builds on existing transformer and color analysis techniques.
The research tackled the problem of distinguishing computer-generated images (CGI) from authentic digital images (ADI) by proposing a method using Swin Transformers and RGB/CbCrY color frame analysis, achieving state-of-the-art accuracy with improvements in processing speed and robustness against manipulations like noise and blurring.
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.