Steering Vision-Language Pre-trained Models for Incremental Face Presentation Attack Detection

• URL: http://arxiv.org/abs/2512.19022v2
• Date: Wed, 24 Dec 2025 07:36:25 GMT
• Title: Steering Vision-Language Pre-trained Models for Incremental Face Presentation Attack Detection
• Authors: Haoze Li, Jie Zhang, Guoying Zhao, Stephen Lin, Shiguang Shan,
• Abstract summary: Face Presentation Attack Detection (PAD) demands incremental learning to combat spoofing tactics and domains.<n>Privacy regulations forbid retaining past data, necessitating rehearsal-free learning (RF-IL)
• Score: 62.89126207012712
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Face Presentation Attack Detection (PAD) demands incremental learning (IL) to combat evolving spoofing tactics and domains. Privacy regulations, however, forbid retaining past data, necessitating rehearsal-free IL (RF-IL). Vision-Language Pre-trained (VLP) models, with their prompt-tunable cross-modal representations, enable efficient adaptation to new spoofing styles and domains. Capitalizing on this strength, we propose \textbf{SVLP-IL}, a VLP-based RF-IL framework that balances stability and plasticity via \textit{Multi-Aspect Prompting} (MAP) and \textit{Selective Elastic Weight Consolidation} (SEWC). MAP isolates domain dependencies, enhances distribution-shift sensitivity, and mitigates forgetting by jointly exploiting universal and domain-specific cues. SEWC selectively preserves critical weights from previous tasks, retaining essential knowledge while allowing flexibility for new adaptations. Comprehensive experiments across multiple PAD benchmarks show that SVLP-IL significantly reduces catastrophic forgetting and enhances performance on unseen domains. SVLP-IL offers a privacy-compliant, practical solution for robust lifelong PAD deployment in RF-IL settings.

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