  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.
