An increasing number of classification approaches have been developed to address the issue of image rebroadcast and recapturing, a standard attack strategy in insurance frauds, face spoofing, and video piracy. However, most of them neglected scale variations and domain generalization scenarios, performing poorly in instances involving domain shifts, typically made worse by inter-domain and cross-domain scale variances. To overcome these issues, we propose a cascaded data augmentation and SWIN transformer domain generalization framework (DAST-DG) in the current research work Initially, we examine the disparity in dataset representation. A feature generator is trained to make authentic images from various domains indistinguishable. This process is then applied to recaptured images, creating a dual adversarial learning setup. Extensive experiments demonstrate that our approach is practical and surpasses state-of-the-art methods across different databases. Our model achieves an accuracy of approximately 82\% with a precision of 95\% on high-variance datasets.

翻译：针对图像重播与重捕获这一保险欺诈、人脸欺骗及视频盗版中的典型攻击手段，已有越来越多的分类方法被提出。然而，现有方法大多忽视了尺度变化与领域泛化场景，在涉及领域偏移的实例中表现不佳，而跨领域与领域间的尺度差异往往进一步加剧了这一问题。为克服这些局限，本研究提出了一种级联数据增强与SWIN Transformer领域泛化框架（DAST-DG）。我们首先分析了数据集表征差异，训练特征生成器使来自不同领域的真实图像难以区分，随后将此过程应用于重捕获图像，构建双重对抗学习机制。大量实验表明，所提方法切实有效，并在多个数据库上超越了现有最优方法。在高度方差数据集上，我们的模型取得了约82\%的准确率与95\%的精确率。