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Face Recognition System (FRS) are shown to be vulnerable to morphed images ofnewborns. Detecting morphing attacks stemming from face images of newborn isimportant to avoid unwanted consequences, both for security and society. Inthis paper, we present a new reference-based/Differential Morphing AttackDetection (MAD) method to detect newborn morphing images using WaveletScattering Network (WSN). We propose a two-layer WSN with 250 $\times$ 250pixels and six rotations of wavelets per layer, resulting in 577 paths. Theproposed approach is validated on a dataset of 852 bona fide images and 2460morphing images constructed using face images of 42 unique newborns. Theobtained results indicate a gain of over 10\% in detection accuracy over otherexisting D-MAD techniques.

Robust Face Morphing Attack Detection Using Fusion of Multiple Features and Classification Techniques