Scale Invariant Domain Generalization Image Recapture Detection
This addresses domain shift and scale variance issues in image recapture detection, which is important for applications like insurance fraud and face identification spoofing, but it appears incremental as it builds on existing detection techniques.
The paper tackles the problem of detecting recaptured images in domain generalization scenarios with scale variances, proposing a scale alignment domain generalization framework (SADG) that achieves better performance compared to state-of-the-art approaches on four databases.
Recapturing and rebroadcasting of images are common attack methods in insurance frauds and face identification spoofing, and an increasing number of detection techniques were introduced to handle this problem. However, most of them ignored the domain generalization scenario and scale variances, with an inferior performance on domain shift situations, and normally were exacerbated by intra-domain and inter-domain scale variances. In this paper, we propose a scale alignment domain generalization framework (SADG) to address these challenges. First, an adversarial domain discriminator is exploited to minimize the discrepancies of image representation distributions among different domains. Meanwhile, we exploit triplet loss as a local constraint to achieve a clearer decision boundary. Moreover, a scale alignment loss is introduced as a global relationship regularization to force the image representations of the same class across different scales to be undistinguishable. Experimental results on four databases and comparison with state-of-the-art approaches show that better performance can be achieved using our framework.