Learning Knowledge-Based Prompts for Robust 3D Mask Presentation Attack Detection
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a novel knowledge-based prompt learning framework for 3D mask presentation attack detection. The method effectively uses vision-language models and knowledge graphs to improve face recognition system security against 3D mask attacks.
Area Of Science
- Computer Science
- Artificial Intelligence
- Cybersecurity
Background
- 3D mask presentation attacks threaten face recognition systems.
- Existing detection methods using multimodal features or remote photoplethysmography (rPPG) are costly and have limited generalization.
- Detection-related text descriptions are cost-effective but underutilized for this task.
Purpose Of The Study
- To explore the potential of vision-language multimodal features for 3D mask presentation attack detection.
- To propose a novel knowledge-based prompt learning framework to enhance detection performance and generalization.
- To address the limitations of current methods by leveraging cost-effective text descriptions and advanced AI techniques.
Main Methods
- Developed a knowledge-based prompt learning framework incorporating knowledge graph entities and triples.
- Introduced a visual-specific knowledge filter using attention mechanisms to refine knowledge graph elements based on visual context.
- Leveraged causal graph theory and a spurious correlation elimination paradigm during training to enhance generalization.
Main Results
- The proposed framework effectively harnesses knowledge from pre-trained vision-language models.
- The method demonstrates strong generalization capabilities for 3D mask presentation attack detection.
- Achieved state-of-the-art performance in both intra- and cross-scenario detection on benchmark datasets.
Conclusions
- Vision-language models, enhanced with knowledge graphs and causal reasoning, offer a powerful approach for 3D mask presentation attack detection.
- The proposed framework provides a cost-effective and highly generalizable solution compared to existing methods.
- This research opens new avenues for securing face recognition systems against sophisticated spoofing attacks.
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