In this paper, we propose a novel approach for conducting face morphing
attacks, which utilizes optimal-landmark-guided image blending. Current face
morphing attacks can be categorized into landmark-based and generation-based
approaches. Landmark-based methods use geometric transformations to warp facial
regions according to averaged landmarks but often produce morphed images with
poor visual quality. Generation-based methods, which employ generation models
to blend multiple face images, can achieve better visual quality but are often
unsuccessful in generating morphed images that can effectively evade
state-of-the-art face recognition systems~(FRSs). Our proposed method overcomes
the limitations of previous approaches by optimizing the morphing landmarks and
using Graph Convolutional Networks (GCNs) to combine landmark and appearance
features. We model facial landmarks as nodes in a bipartite graph that is fully
connected and utilize GCNs to simulate their spatial and structural
relationships. The aim is to capture variations in facial shape and enable
accurate manipulation of facial appearance features during the warping process,
resulting in morphed facial images that are highly realistic and visually
faithful. Experiments on two public datasets prove that our method inherits the
advantages of previous landmark-based and generation-based methods and
generates morphed images with higher quality, posing a more significant threat
to state-of-the-art FRSs.

我们提出了一种利用最优地标引导的图像融合进行人脸变形攻击的新方法，通过优化变形地标并使用图卷积网络（GCNs）结合地标和外观特征的方式，生成更高质量的变形图像，对最先进的人脸识别系统构成更大的威胁。

面部融合攻击的最优地标引导图像混合

Optimal-Landmark-Guided Image Blending for Face Morphing Attacks

In contrast to the abundant research focusing on large-scale models, the
progress in lightweight semantic segmentation appears to be advancing at a
comparatively slower pace. However, existing compact methods often suffer from
limited feature representation capability due to the shallowness of their
networks. In this paper, we propose a novel lightweight segmentation
architecture, called Multi-scale Feature Propagation Network (MFPNet), to
address the dilemma. Specifically, we design a robust Encoder-Decoder structure
featuring symmetrical residual blocks that consist of flexible bottleneck
residual modules (BRMs) to explore deep and rich muti-scale semantic context.
Furthermore, taking benefit from their capacity to model latent long-range
contextual relationships, we leverage Graph Convolutional Networks (GCNs) to
facilitate multi-scale feature propagation between the BRM blocks. When
evaluated on benchmark datasets, our proposed approach shows superior
segmentation results.

我们提出了一种新颖的轻量级分割架构，名为多尺度特征传播网络 (MFPNet)，通过设计具有柔性瓶颈残差模块的鲁棒的编码器 - 解码器结构，探索深入且丰富的多尺度语义上下文，同时利用图卷积网络 (GCNs)，在瓶颈残差模块之间进行多尺度特征传播，最终在基准数据集上表现出优异的分割结果。

MFPNet: 轻量级语义分割的多尺度特征传播网络

MFPNet: Multi-scale Feature Propagation Nwtwork For Lightweight Semantic  Segmentation

Gait recognition is a promising biometric with unique properties for
identifying individuals from a long distance by their walking patterns. In
recent years, most gait recognition methods used the person's silhouette to
extract the gait features. However, silhouette images can lose fine-grained
spatial information, suffer from (self) occlusion, and be challenging to obtain
in real-world scenarios. Furthermore, these silhouettes also contain other
visual clues that are not actual gait features and can be used for
identification, but also to fool the system. Model-based methods do not suffer
from these problems and are able to represent the temporal motion of body
joints, which are actual gait features. The advances in human pose estimation
started a new era for model-based gait recognition with skeleton-based gait
recognition. In this work, we propose an approach based on Graph Convolutional
Networks (GCNs) that combines higher-order inputs, and residual networks to an
efficient architecture for gait recognition. Extensive experiments on the two
popular gait datasets, CASIA-B and OUMVLP-Pose, show a massive improvement (3x)
of the state-of-the-art (SotA) on the largest gait dataset OUMVLP-Pose and
strong temporal modeling capabilities. Finally, we visualize our method to
understand skeleton-based gait recognition better and to show that we model
real gait features.

该论文提出了一种基于 Graph Convolutional Networks（GCNs）的方法，结合高级别输入和残差网络，用于骨架轮廓图进行步态识别，实验结果显示了 3 倍于最先进技术水平和强大的视频建模能力。