用视觉变压器+色彩分析，高效区分真实与生成图像。
Enhancing Image Authenticity Detection: Swin Transformers and Color Frame Analysis for CGI vs. Real Images
- 采用Swin Transformer直接处理颜色通道数据，无需手工特征
- 在多种图像篡改下仍保持高准确率，速度优于现有方法
- 适合需要快速检测深度伪造图像的场景
计算机图形技术的快速发展使生成图像（CGI）质量大幅提升，已接近真实拍摄图像（ADI），导致二者难以区分，加剧了虚假信息传播风险。本文提出一种新方法，结合Swin Transformers与RGB和CbCrY色彩通道分析进行预处理，通过直接利用像素数据训练模型，摒弃传统手工特征。该方法在多种联合图像篡改（如加噪、模糊、JPEG压缩）条件下表现出色，达到当前最优分类准确率，同时显著提升处理速度与鲁棒性。结果表明，Swin Transformer与先进色彩分析相结合，在高效且可靠的图像真实性检测中具有巨大潜力。
原文摘要 · Abstract (English)
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.
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