ColFigPhotoAttnNet: Reliable Finger Photo Presentation Attack Detection Leveraging Window-Attention on Color Spaces
Journal:
arXiv
Published Date:
Mar 7, 2025
Abstract
Finger photo Presentation Attack Detection (PAD) can significantly strengthen
smartphone device security. However, these algorithms are trained to detect
certain types of attacks. Furthermore, they are designed to operate on images
acquired by specific capture devices, leading to poor generalization and a lack
of robustness in handling the evolving nature of mobile hardware. The proposed
investigation is the first to systematically analyze the performance
degradation of existing deep learning PAD systems, convolutional and
transformers, in cross-capture device settings. In this paper, we introduce the
ColFigPhotoAttnNet architecture designed based on window attention on color
channels, followed by the nested residual network as the predictor to achieve a
reliable PAD. Extensive experiments using various capture devices, including
iPhone13 Pro, GooglePixel 3, Nokia C5, and OnePlusOne, were carried out to
evaluate the performance of proposed and existing methods on three publicly
available databases. The findings underscore the effectiveness of our approach.
