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

翻译：人脸识别系统（FRS）已被证明易受新生儿形变图像的攻击。检测源自新生儿人脸图像的形变攻击，对于避免安全与社会领域的不良后果至关重要。本文提出一种基于参考/差分形变攻击检测（Differential Morphing Attack Detection, D-MAD）的新方法，利用小波散射网络（Wavelet Scattering Network, WSN）检测新生儿形变图像。我们构建了一个两层WSN，每层采用250×250像素及六个方向的小波旋转，共生成577条路径。该方法在包含852张真实图像和2460张形变图像的数据集上进行了验证，该数据集由42名独特性新生儿的正面图像构建而成。实验结果表明，与现有其他D-MAD技术相比，本方法在检测准确率上提升超过10%。