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Dynamic Cross-Modal Modeling with an Ultra-Lightweight Architecture for Face Anti-Spoofing.

Nana Li¹, Jiayu Wang¹, Zuhe Li¹

¹School of Computer Science and Artificial Intelligence, Zhengzhou University of Light Industry, Zhengzhou 450002, China.

Sensors (Basel, Switzerland)

|May 13, 2026

Summary

This summary is machine-generated.

This study introduces an ultra-lightweight framework for dynamic face anti-spoofing. It achieves high accuracy and real-time performance on edge devices with minimal computational cost.

Keywords:

dynamic cross-modal modeling face anti-spoofing lightweight architecture multimodal fusion

Related Experiment Videos

Area of Science:

Computer Vision
Biometrics
Artificial Intelligence

Background:

Effective face anti-spoofing relies on modeling cross-modal relationships.
Existing methods struggle with dynamic modality importance and computational constraints.

Purpose of the Study:

To propose an ultra-lightweight dynamic cross-modal framework for face anti-spoofing.
To enable real-time edge inference with minimal computational resources.

Main Methods:

Enhanced ShuffleNetV2 with Ghost-Generated Shuffle BlockA (GGS-BlockA) for feature extraction.
Lightweight Cross-Modal Attention (LCMA) for dynamic modality reweighting.
Lightweight Cross-Modal Fusion (LCMF) using depth cues for alignment.

Main Results:

Achieved 0.064% Average Classification Error Rate (ACER) on CASIA-SURF.
Utilized only 0.14M parameters and 0.0065G FLOPs.
Reached 99.86% detection rate at TPR@FPR=10-4.

Conclusions:

The proposed framework offers a highly efficient and robust solution for face anti-spoofing.
Demonstrates strong generalization capability under strict computational limitations.