Combining Identity Features and Artifact Analysis for Differential Morphing Attack Detection

被引:2
|
作者
Di Domenico, Nicolo [1 ]
Borghi, Guido [1 ]
Franco, Annalisa [1 ]
Maltoni, Davide [1 ]
机构
[1] Univ Bologna, Dipartimento Informat Sci & Ingn DISI, I-47521 Cesena, Italy
来源
IMAGE ANALYSIS AND PROCESSING, ICIAP 2023, PT I | 2023年 / 14233卷
基金
欧盟地平线“2020”;
关键词
Morphing Attack; Morphing Attack Detection; Differential MAD (D-MAD); Single image MAD (S-MAD); Feature Fusion;
D O I
10.1007/978-3-031-43148-7_9
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Due to the importance of the Morphing Attack, the development of new and accurate Morphing Attack Detection (MAD) systems is urgently needed by private and public institutions. In this context, D-MAD methods, i.e. detectors fed with a trusted live image and a probe tend to show better performance with respect to S-MAD approaches, that are based on a single input image. However, D-MAD methods usually leverage the identity of the two input face images only, and then present two main drawbacks: they lose performance when the two subjects look alike, and they do not consider potential artifacts left by the morphing procedure (which are instead typically exploited by S-MAD approaches). Therefore, in this paper, we investigate the combined use of D-MAD and S-MAD to improve detection performance through the fusion of the features produced by these two MAD approaches.
引用
收藏
页码:100 / 111
页数:12
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