Open Access

ARTICLE

PNSS: Unknown Face Presentation Attack Detection with Pseudo Negative Sample Synthesis

Hongyang Wang^1,2, Yichen Shi³, Jun Feng^1,2,*, Zitong Yu⁴, Zhuofu Tao⁵

1 School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China
2 Shijiazhuang Key Laboratory of Artificial Intelligence, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China
3 School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
4 School of Computer Science and Technology, Great Bay University, Dongguan, 523808, China
5 School of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA

* Corresponding Author: Jun Feng. Email:

(This article belongs to the Special Issue: Multimedia Security in Deep Learning)

Computers, Materials & Continua 2025, 83(2), 3097-3112. https://doi.org/10.32604/cmc.2025.061019

Received 14 November 2024; Accepted 11 February 2025; Issue published 16 April 2025

Abstract

Face Presentation Attack Detection (fPAD) plays a vital role in securing face recognition systems against various presentation attacks. While supervised learning-based methods demonstrate effectiveness, they are prone to overfitting to known attack types and struggle to generalize to novel attack scenarios. Recent studies have explored formulating fPAD as an anomaly detection problem or one-class classification task, enabling the training of generalized models for unknown attack detection. However, conventional anomaly detection approaches encounter difficulties in precisely delineating the boundary between bonafide samples and unknown attacks. To address this challenge, we propose a novel framework focusing on unknown attack detection using exclusively bonafide facial data during training. The core innovation lies in our pseudo-negative sample synthesis (PNSS) strategy, which facilitates learning of compact decision boundaries between bonafide faces and potential attack variations. Specifically, PNSS generates synthetic negative samples within low-likelihood regions of the bonafide feature space to represent diverse unknown attack patterns. To overcome the inherent imbalance between positive and synthetic negative samples during iterative training, we implement a dual-loss mechanism combining focal loss for classification optimization with pairwise confusion loss as a regularizer. This architecture effectively mitigates model bias towards bonafide samples while maintaining discriminative power. Comprehensive evaluations across three benchmark datasets validate the framework’s superior performance. Notably, our PNSS achieves 8%–18% average classification error rate (ACER) reduction compared with state-of-the-art one-class fPAD methods in cross-dataset evaluations on Idiap Replay-Attack and MSU-MFSD datasets.

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Keywords

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