Scale Invariant Domain Generalization Image Recapture Detection
- URL: http://arxiv.org/abs/2110.03496v1
- Date: Thu, 7 Oct 2021 14:32:56 GMT
- Title: Scale Invariant Domain Generalization Image Recapture Detection
- Authors: Jinian Luo, Jie Guo, Weidong Qiu, Zheng Huang, and Hong Hui
- Abstract summary: 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.
Finally, 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.
- Score: 3.210092699356333
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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