人脸变形攻击检测中深度特征的多级融合

Sushma Venktatesh
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引用次数: 1

摘要

面部生物识别系统广泛应用于包括边境控制在内的大量安全相关应用。然而,人脸识别系统(FRS)对各种类型的攻击的脆弱性得到了很好的证明。这项工作提出了一种新的人脸变形攻击检测方法,该方法通过执行深度特征的多层次融合来实现单图像场景。使用预训练的深度cnn(如AlexNet、ResNet50)提取特征。这些提取的特征在特征和得分水平上结合起来,得出给定的人脸图像是否为变形。在使用五种不同的人脸变形生成技术和三种不同的数据介质构建的人脸变形数据集上,对所提出的单图像变形攻击检测(S-MAD)方法进行了广泛的评估。数据介质包括数字化、打印扫描(再数字化)、打印扫描压缩(再数字化和压缩)。使用内部(用于培训和测试的相同数据类型)和内部评估场景(用于培训和测试的交叉数据类型)进行了广泛的实验。此外,将该方法与基于无参考/单图像变形攻击检测(S-MAD)的最先进(SOTA)方法进行了比较。统计分析表明,所提出的方法在所有三种不同的媒介中表现最佳。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multilevel Fusion of Deep Features for Face Morphing Attack Detection
Face biometric systems are widely deployed in a magnitude of security-related applications, including border control. However, the vulnerability of the Face Recognition Systems(FRS) to various types of attacks is well demonstrated. This work presents a novel approach for face morphing attack detection for a single image scenario by performing a multi-level fusion of deep features. The features are extracted using the pre-trained deep CNNs such as AlexNet, ResNet50. These extracted features are combined at both features and score levels to conclude if the given face image is a morph. The proposed single image Morph Attack Detection (S-MAD) approach is extensively evaluated on the face morphing dataset constructed using five different face morphing generation techniques and three different data mediums. The data mediums including digital, print-scan (re-digitised), print-scan compression (re-digitised and compressed.) Extensive experiments are carried out with intra (same datatype used for training and testing) and inter-evaluation scenarios (cross datatype used for training and testing). Further, the proposed method is compared with the State-Of-The-Art (SOTA) approaches for No reference-based/Single image Morph Attack Detection (S-MAD). The statistical analysis indicates the best performance of the proposed approach in all three different mediums.
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