Scale Invariant Domain Generalization Image Recapture Detection

TL;DR

This paper introduces a scale alignment domain generalization framework (SADG) for image recapture detection, effectively handling domain shifts and scale variances to improve detection accuracy across diverse datasets.

Contribution

The paper proposes a novel SADG framework combining adversarial domain discrimination, triplet loss, and scale alignment loss for robust recapture detection under domain shifts.

Findings

Outperforms state-of-the-art methods on four databases.

Effectively handles scale variances and domain shifts.

Improves detection accuracy in recaptured images.

Abstract

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.