Enhanced Multi Attention-Net with Frequency-Aware Squeezed Residual Blocks for Robust Face Anti-Spoofing
DOI:
https://doi.org/10.15662/IJEETR.2026.0802075Keywords:
Enhanced Multi Attention Network, Frequency-Aware Residual Blocks, Face Anti-Spoofing, Deep Learning, Attention Mechanism, Frequency Analysis, Liveness DetectionAbstract
Face recognition systems are widely deployed in authentication, surveillance, and financial applications. However, these systems remain highly vulnerable to presentation attacks such as printed photos, replayed videos, and 3D masks. Existing CNN-based anti-spoofing approaches often suffer from poor cross-dataset generalization due to environmental variations and unseen spoof patterns.
This paper proposes an Enhanced Multi-Attention Network with Frequency-Aware Squeezed Residual Blocks (EMAN-FASRB) to improve robustness against face spoofing attacks. The proposed architecture integrates spatial, channel, and frequency attention mechanisms with lightweight residual learning to capture both spatial textures and frequency-domain spoof artifacts. Experiments conducted on benchmark datasets including CASIA-FASD, Replay-Attack, and MSU-MFSD demonstrate improved performance in terms of EER, HTER, ACER, and AUC. Results confirm superior cross-dataset generalization and computational efficiency compared to baseline CNN models
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