More than just an auxiliary loss: Anti-spoofing Backbone Training via
Adversarial Pseudo-depth Generation
- URL: http://arxiv.org/abs/2101.00200v2
- Date: Fri, 19 Mar 2021 04:57:42 GMT
- Title: More than just an auxiliary loss: Anti-spoofing Backbone Training via
Adversarial Pseudo-depth Generation
- Authors: Chang Keun Paik, Naeun Ko, Youngjoon Yoo
- Abstract summary: A new method of training pipeline is discussed to achieve significant performance on the task of anti-spoofing with RGB image.
Our method approaches the baseline performance of the current state of the art anti-spoofing models with 15.8x less parameters used.
- Score: 4.542003078412816
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this paper, a new method of training pipeline is discussed to achieve
significant performance on the task of anti-spoofing with RGB image. We explore
and highlight the impact of using pseudo-depth to pre-train a network that will
be used as the backbone to the final classifier. While the usage of
pseudo-depth for anti-spoofing task is not a new idea on its own, previous
endeavours utilize pseudo-depth simply as another medium to extract features
for performing prediction, or as part of many auxiliary losses in aiding the
training of the main classifier, normalizing the importance of pseudo-depth as
just another semantic information. Through this work, we argue that there
exists a significant advantage in training the final classifier can be gained
by the pre-trained generator learning to predict the corresponding pseudo-depth
of a given facial image, from a Generative Adversarial Network framework. Our
experimental results indicate that our method results in a much more adaptable
system that can generalize beyond intra-dataset samples, but to inter-dataset
samples, which it has never seen before during training. Quantitatively, our
method approaches the baseline performance of the current state of the art
anti-spoofing models with 15.8x less parameters used. Moreover, experiments
showed that the introduced methodology performs well only using basic binary
label without additional semantic information which indicates potential
benefits of this work in industrial and application based environment where
trade-off between additional labelling and resources are considered.
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