提出新型注意力网络，提升手机指纹照片攻击检测的跨设备泛化能力
ColFigPhotoAttnNet: Reliable Finger Photo Presentation Attack Detection Leveraging Window-Attention on Color Spaces
- 基于颜色通道窗口注意力机制，增强特征捕捉能力
- 在多款手机设备上测试，显著优于现有方法
- 适合需要跨设备鲁棒性的生物识别安全系统
指纹照片攻击检测（PAD）可显著增强智能手机设备安全。然而，现有算法通常针对特定攻击类型训练，且依赖特定采集设备，导致泛化能力差，难以应对移动硬件快速演进。本文首次系统分析了现有深度学习PAD系统（包括卷积与Transformer模型）在跨设备场景下的性能退化问题。提出ColFigPhotoAttnNet架构，基于颜色通道的窗口注意力机制，后接嵌套残差网络作为分类器，实现可靠PAD检测。在iPhone13 Pro、Google Pixel 3、Nokia C5和OnePlus One等多款设备上，于三个公开数据库进行大量实验，验证了该方法的有效性。
原文摘要 · Abstract (English)
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.
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