生成超10万张高质量人脸合成变形数据，解决隐私难题
SynMorph: Generating Synthetic Face Morphing Dataset with Mated Samples
- 用合成方法生成2450人、超10万张人脸变形图像
- 数据集涵盖多种变形算法，支持单/差分攻击检测
- 适合训练和评测防伪造人脸识别系统
人脸变形攻击检测（MAD）算法已成为应对人脸识别系统漏洞的关键手段。由于隐私顾虑和数据限制，现有大规模公开数据集稀缺。本文提出一种新方法，生成包含2450个身份、超过10万张变形图像的合成人脸变形数据集。该数据集具有高质量样本、多样化的变形算法，并能有效泛化于单模态与差分式变形攻击检测算法。通过人脸图像质量评估与漏洞分析，从生物特征样本质量及对人脸识别系统的攻击潜力角度验证其有效性。实验结果在现有SOTA合成数据集及代表性非合成数据集上进行基准测试，显示性能提升。此外，设计多种训练协议，研究该合成数据集在训练变形攻击检测算法中的适用性。
原文摘要 · Abstract (English)
Face morphing attack detection (MAD) algorithms have become essential to overcome the vulnerability of face recognition systems. To solve the lack of large-scale and public-available datasets due to privacy concerns and restrictions, in this work we propose a new method to generate a synthetic face morphing dataset with 2450 identities and more than 100k morphs. The proposed synthetic face morphing dataset is unique for its high-quality samples, different types of morphing algorithms, and the generalization for both single and differential morphing attack detection algorithms. For experiments, we apply face image quality assessment and vulnerability analysis to evaluate the proposed synthetic face morphing dataset from the perspective of biometric sample quality and morphing attack potential on face recognition systems. The results are benchmarked with an existing SOTA synthetic dataset and a representative non-synthetic and indicate improvement compared with the SOTA. Additionally, we design different protocols and study the applicability of using the proposed synthetic dataset on training morphing attack detection algorithms.
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