论文标题
基于融合的几个射击变形攻击检测和指纹
Fusion-based Few-Shot Morphing Attack Detection and Fingerprinting
论文作者
论文摘要
面部识别系统对变形攻击的脆弱性构成了严重的安全威胁，因为在现实世界中广泛采用了面部生物识别技术。大多数现有的变形攻击检测（MAD）方法都需要大量的训练数据，并且仅在一些预定义的攻击模型上进行了测试。缺乏良好的概括属性，尤其是考虑到对发展新型变形攻击的兴趣日益增长的兴趣，这是现有的MAD研究的一个关键限制。为了解决这个问题，我们建议在本文中从监督学习到几个二进制检测到多类指纹的疯狂。我们的技术贡献包括：1）我们提出了一种基于融合的少量学习方法（FSL）方法来学习判别特征，这些特征可以推广到从预定义的演示攻击中看不见的变形攻击类型； 2）根据PRNU模型和Noiseprint网络的融合，提出的FSL从二进制MAD扩展到多类变形攻击指纹（MAF）。 3）我们收集了一个大型数据库，其中包含五个面部数据集和八种不同的变形算法，以基准提出的少量射击MAF（FS-MAF）方法。广泛的实验结果表明，我们基于融合的FS-MAF的出色表现。代码和数据将在https://github.com/nz0001na/mad Maf上公开获取。
The vulnerability of face recognition systems to morphing attacks has posed a serious security threat due to the wide adoption of face biometrics in the real world. Most existing morphing attack detection (MAD) methods require a large amount of training data and have only been tested on a few predefined attack models. The lack of good generalization properties, especially in view of the growing interest in developing novel morphing attacks, is a critical limitation with existing MAD research. To address this issue, we propose to extend MAD from supervised learning to few-shot learning and from binary detection to multiclass fingerprinting in this paper. Our technical contributions include: 1) We propose a fusion-based few-shot learning (FSL) method to learn discriminative features that can generalize to unseen morphing attack types from predefined presentation attacks; 2) The proposed FSL based on the fusion of the PRNU model and Noiseprint network is extended from binary MAD to multiclass morphing attack fingerprinting (MAF). 3) We have collected a large-scale database, which contains five face datasets and eight different morphing algorithms, to benchmark the proposed few-shot MAF (FS-MAF) method. Extensive experimental results show the outstanding performance of our fusion-based FS-MAF. The code and data will be publicly available at https://github.com/nz0001na/mad maf.
