使用 SWIN Transformer 的域泛化重拍屏幕图像识别
Domain Generalized Recaptured Screen Image Identification Using SWIN Transformer
AI总结：
针对图像重拍识别中域偏移和尺度变化导致性能下降的问题，提出级联数据增强与SWIN transformer域泛化框架（DAST-DG），通过双重对抗学习对齐特征，在多数据库上超越现有方法并实现高精度。
摘要：
为了解决图像重播和重拍这一保险欺诈、人脸欺骗和视频盗版中的标准攻击策略问题，人们开发了越来越多的分类方法。然而，其中大多数方法忽略了尺度变化和域泛化场景，在涉及域偏移的情况下表现不佳，而域间和跨域尺度差异通常会使这种情况更加恶化。为了克服这些问题，我们在当前研究工作中提出了一种级联数据增强和 SWIN transformer 域泛化框架（DAST-DG）。首先，我们检查了数据集表示中的差异。训练了一个特征生成器，使来自不同域的真实图像变得不可区分。然后将此过程应用于重拍图像，创建了双重对抗学习设置。大量实验表明，我们的方法具有实用性，并在不同数据库上超越了最先进的方法。我们的模型在高方差数据集上实现了约 82% 的准确率和 95% 的精度。
英文摘要：
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.
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