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.

本文提出一种基于参考的 / 差分变形攻击检测（MAD）方法，使用小波散射网络（WSN）检测新生儿变形的图像，得到的结果表明，相较于现有的 D-MAD 技术，检测准确率提高了 10% 以上。

使用多特征和分类技术融合检测稳健的人脸混合攻击

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

通过使用小波散射网络（WSN）提出了一种基于参考的 / 差分变形攻击检测（MAD）方法来检测新生儿变形图像，验证结果表明比其他现有的 D-MAD 技术提高了超过 10％的检测精度。

利用小波散射网络检测差异性新生儿面孔变形攻击

Differential Newborn Face Morphing Attack Detection using Wavelet  Scatter Network

Recently, synthetic aperture radar (SAR) image change detection has become an
interesting yet challenging direction due to the presence of speckle noise.
Although both traditional and modern learning-driven methods attempted to
overcome this challenge, deep convolutional neural networks (DCNNs)-based
methods are still hindered by the lack of interpretability and the requirement
of large computation power. To overcome this drawback, wavelet scattering
network (WSN) and Fourier scattering network (FSN) are proposed. Combining
respective merits of WSN and FSN, we propose Stockwell scattering network (SSN)
based on Stockwell transform which is widely applied against noisy signals and
shows advantageous characteristics in speckle reduction. The proposed SSN
provides noise-resilient feature representation and obtains state-of-art
performance in SAR image change detection as well as high computational
efficiency. Experimental results on three real SAR image datasets demonstrate
the effectiveness of the proposed method.

本研究利用 Stockwell 变换提出了 Stockwell 散射网络（SSN）作为一种合成孔径雷达（SAR）图像变化检测的方法，该方法结合了 WS​​N 和 FSN 的优点，并在 SAR 图像变化检测方面取得了最新的技术成果。实验结果表明，所提出的方法在三个真实的 SAR 图像数据集上表现出了很好的性能。

基于 Stockwell 散射网络的 SAR 图像变化检测

SSN: Stockwell Scattering Network for SAR Image Change Detection

A wavelet scattering network computes a translation invariant image
representation, which is stable to deformations and preserves high frequency
information for classification. It cascades wavelet transform convolutions with
non-linear modulus and averaging operators. The first network layer outputs
SIFT-type descriptors whereas the next layers provide complementary invariant
information which improves classification. The mathematical analysis of wavelet
scattering networks explains important properties of deep convolution networks
for classification.
A scattering representation of stationary processes incorporates higher order
moments and can thus discriminate textures having the same Fourier power
spectrum. State of the art classification results are obtained for handwritten
digits and texture discrimination, using a Gaussian kernel SVM and a generative
PCA classifier.

利用小波散射网络对静态过程进行表示，获得更高阶矩并可用于区分具有相同傅立叶功率谱的纹理，对于手写数字和纹理判别任务取得了最先进的分类结果。