E-STDN: Efficient spoof trace disentanglement for face anti-spoofing systems using lightweight models
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DOI:
https://doi.org/10.15625/1813-9663/23599Keywords:
Face anti-spoofing, E-STDN, spoof trace learning, MobileNet.Abstract
This paper introduces E-STDN, an efficient architecture for face anti-spoofing that enhances the original spoof trace disentanglement network (STDN) by addressing the trade-off between accuracy and computation cost. Inspired by MobileNet, our design remains lightweight while maintaining strong spoof trace representation capabilities. Experiments on the CelebA-Spoof dataset demonstrate that E-STDN achieves superior TDR@FDR=0.5 compared to previous baselines, while significantly reducing inference time relative to the original STDN. Overall, our work presents an improved model design that can contribute to the development of robust and practical face anti-spoofing systems.
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Published
07-07-2026
How to Cite
[1]Minh Pham Ngoc, Trung Pham Duy, and Lam Thu Bui, “E-STDN: Efficient spoof trace disentanglement for face anti-spoofing systems using lightweight models”, J. Comput. Sci. Cybern., Jul. 2026.
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