CPG-PAD: Concept-Informed Prompts Guided Presentation Attack Detection
Proposes CPG-PAD, a framework that introduces model-level concept guidance into prompt learning for Presentation Attack Detection. It uses XAI to discover attack-relevant visual concepts and injects them into prompts, achieving state-of-the-art cross-domain performance on nine benchmarks.
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[Submitted on 1 Jul 2026]
Title:CPG-PAD: Concept-Informed Prompts Guided Presentation Attack Detection
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Abstract:Presentation Attack Detection (PAD) serves as a crucial safeguard for face recognition systems against presentation attacks such as printed photos, replayed videos, and 3D masks. Despite significant progress, existing PAD models still struggle to generalize across unseen domains due to variations in sensors, lighting, and attack materials. Recent Vision-Language Models (VLMs) have shown strong generalization ability, yet their applications in PAD remain limited because learned prompts, typically optimized under class-label supervision, fail to explicitly align with fine-grained attack-relevant visual semantics. As a result, the learned representations often overfit domain-specific artifacts instead of capturing transferable attack cues. To address this, we propose Concept-Informed Prompts Guided Presentation Attack Detection (CPG-PAD), a framework that introduces model-level concept guidance into the prompt learning process. Specifically, we design a Visual Concept-driven Enhancement (VCE) module that employs eXplainable AI (XAI) techniques to automatically discover PAD-relevant visual concepts and generate concept-associated heatmaps providing localized fine-grained guidance. Guided by these heatmaps, a Prompt-based Concept Injection (PCI) mechanism integrates these concepts into the prompt space through a Visual-Prompt Decoder (VPD) and a concept-mapping loss, enabling prompts to align with the model's internal concept space. This design enables CPG-PAD to capture generalizable and domain-invariant attack cues while effectively suppressing dataset-specific biases. Extensive experiments across nine benchmark datasets demonstrate that CPG-PAD consistently achieves state-of-the-art cross-domain performance under multi-source, limited-source, and single-source settings.
Comments: Accepted by IEEE Transactions on Information Forensics & Security (TIFS)
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.01303 [cs.CV]
(or arXiv:2607.01303v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.01303
arXiv-issued DOI via DataCite
Submission history
From: Haoyuan Zhang [view email] [v1] Wed, 1 Jul 2026 15:33:45 UTC (1,945 KB)
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