用集成注意力图解释人脸活体检测模型决策，提升可信度
Explainable Face Presentation Attack Detection via Ensemble-CAM
- 通过集成多个注意力图生成更稳定的视觉解释
- 可定位模型判断真假脸的关键区域，提升透明性
- 适合需要可解释性的安全系统开发者与审核人员
呈现攻击是利用伪造生物特征数据（如人脸、指纹或虹膜图像）获取未授权访问的重大安全威胁。尽管深度学习（DL）模型在人脸活体检测（PAD）中表现优异，但多数模型如同黑箱，决策过程不透明。可解释性技术旨在揭示模型行为背后的原因。本文提出一种新型可视化解释方法Ensemble-CAM，用于解析基于深度学习的人脸PAD系统的决策依据。该方法通过融合多个注意力图，生成更稳定、可靠的视觉解释，帮助识别模型判定图像为真实或伪造的关键区域。本研究旨在提升深度学习人脸PAD系统的可解释性与可信度，推动其在高安全场景中的应用。
原文摘要 · Abstract (English)
Presentation attacks represent a critical security threat where adversaries use fake biometric data, such as face, fingerprint, or iris images, to gain unauthorized access to protected systems. Various presentation attack detection (PAD) systems have been designed leveraging deep learning (DL) models to mitigate this type of threat. Despite their effectiveness, most of the DL models function as black boxes - their decisions are opaque to their users. The purpose of explainability techniques is to provide detailed information about the reason behind the behavior or decision of DL models. In particular, visual explanation is necessary to better understand the decisions or predictions of DL-based PAD systems and determine the key regions due to which a biometric image is considered real or fake by the system. In this work, a novel technique, Ensemble-CAM, is proposed for providing visual explanations for the decisions made by deep learning-based face PAD systems. Our goal is to improve DL-based face PAD systems by providing a better understanding of their behavior. Our provided visual explanations will enhance the transparency and trustworthiness of DL-based face PAD systems.
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