Learning Knowledge-based Prompts for Robust 3D Mask Presentation Attack Detection

TL;DR

This paper introduces a knowledge-based prompt learning framework leveraging vision-language models and knowledge graphs to improve the robustness and generalization of 3D mask presentation attack detection, outperforming existing methods.

Contribution

It proposes a novel prompt learning approach that incorporates knowledge graphs and causal reasoning, enhancing detection accuracy and generalization in 3D mask attack scenarios.

Findings

Achieves state-of-the-art performance on benchmark datasets.

Improves cross-scenario detection robustness.

Effectively filters irrelevant features using knowledge-guided attention.

Abstract

3D mask presentation attack detection is crucial for protecting face recognition systems against the rising threat of 3D mask attacks. While most existing methods utilize multimodal features or remote photoplethysmography (rPPG) signals to distinguish between real faces and 3D masks, they face significant challenges, such as the high costs associated with multimodal sensors and limited generalization ability. Detection-related text descriptions offer concise, universal information and are cost-effective to obtain. However, the potential of vision-language multimodal features for 3D mask presentation attack detection remains unexplored. In this paper, we propose a novel knowledge-based prompt learning framework to explore the strong generalization capability of vision-language models for 3D mask presentation attack detection. Specifically, our approach incorporates entities and triples from knowledge graphs into the prompt learning process, generating fine-grained, task-specific explicit prompts that effectively harness the knowledge embedded in pre-trained vision-language models. Furthermore, considering different input images may emphasize distinct knowledge graph elements, we introduce a visual-specific knowledge filter based on an attention mechanism to refine relevant elements according to the visual context. Additionally, we leverage causal graph theory insights into the prompt learning process to further enhance the generalization ability of our method. During training, a spurious correlation elimination paradigm is employed, which removes category-irrelevant local image patches using guidance from knowledge-based text features, fostering the learning of generalized causal prompts that align with category-relevant local patches. Experimental results demonstrate that the proposed method achieves state-of-the-art intra- and cross-scenario detection performance on benchmark datasets.