SFAD-Net: A Dual-Stream Spatial–Frequency Attention Network for Fingerprint Presentation Attack Detection

Abstract

Fingerprint presentation attack detection (PAD) remains an open problem owing to continually developing spoofing schemes and large variations in sensors and acquisition conditions. In this paper, we propose SFAD-Net, a lightweight dual-stream spatial–frequency attention network for fingerprint presentation attack detection. The framework integrates complementary spatial and wavelet-based frequency representations with a lightweight attention-guided fusion mechanism to improve discriminative feature learning while maintaining computational efficiency. We evaluate SFAD-Net on the LivDet 2011, 2013, and 2015 datasets using the standard intra-sensor evaluation protocol, together with an additional cross-sensor evaluation on the LivDet 2015 dataset to investigate the impact of sensor-induced domain shifts. The results demonstrate that the proposed method achieves an accuracy of 99.51% on the LivDet 2011 Sagem sensor while maintaining strong intra-sensor presentation attack detection performance across the independently evaluated LivDet benchmark datasets. At the dataset level, SFAD-Net achieves mean ACER values of 1.56%, 1.17%, and 2.60% for LivDet 2011, 2013, and 2015, respectively, together with low APCER and BPCER across the evaluated sensors. The cross-sensor evaluation further highlights the challenges posed by sensor-induced distribution shifts and provides additional insight into the behavior of the proposed framework under unseen sensor conditions. Extensive ablation studies systematically evaluate the contribution of the dual-stream design, wavelet-based frequency representation, and attention-guided feature fusion. In addition to its detection performance, SFAD-Net contains 0.41 M parameters, requires 2.56 GFLOPs, and has a model size of 4.86 MB, achieving 12.31 FPS in the local computational benchmarking setup. Direct deployment on a 4 GB Raspberry Pi 5 further provides evidence of its practical feasibility. Among the evaluated reduced-precision representations, FP16 substantially reduces model size while preserving PAD performance close to FP32, whereas INT8 achieves 35.99 ms latency and 27.79 FPS but introduces a noticeable degradation in PAD performance under the evaluated LivDet 2015 Crossmatch condition.