Domain Generalized Recaptured Screen Image Identification Using SWIN Transformer
This addresses image recapturing attacks in fraud and piracy, but it is incremental as it builds on existing domain generalization methods.
The paper tackles the problem of identifying recaptured screen images under domain shifts and scale variations, proposing a cascaded data augmentation and SWIN transformer framework that achieves approximately 82% accuracy and 95% precision on high-variance datasets.
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.