With the popularity of social media platforms such as Instagram and TikTok,
and the widespread availability and convenience of retouching tools, an
increasing number of individuals are utilizing these tools to beautify their
facial photographs. This poses challenges for fields that place high demands on
the authenticity of photographs, such as identity verification and social
media. By altering facial images, users can easily create deceptive images,
leading to the dissemination of false information. This may pose challenges to
the reliability of identity verification systems and social media, and even
lead to online fraud. To address this issue, some work has proposed makeup
removal methods, but they still lack the ability to restore images involving
geometric deformations caused by retouching. To tackle the problem of facial
retouching restoration, we propose a framework, dubbed Face2Face, which
consists of three components: a facial retouching detector, an image
restoration model named FaceR, and a color correction module called
Hierarchical Adaptive Instance Normalization (H-AdaIN). Firstly, the facial
retouching detector predicts a retouching label containing three integers,
indicating the retouching methods and their corresponding degrees. Then FaceR
restores the retouched image based on the predicted retouching label. Finally,
H-AdaIN is applied to address the issue of color shift arising from diffusion
models. Extensive experiments demonstrate the effectiveness of our framework
and each module.

通过提出的 Face2Face 框架，包括面部修饰检测器、基于预测修饰标签的图像修复模型 FaceR 和用于处理扩散模型引起的色彩偏移的分层自适应实例归一化（H-AdaIN）模块，来解决面部修饰还原问题，并通过实验验证了我们的框架和每个模块的有效性。

Face2Face：基于标签的面部修饰恢复

Face2Face: Label-driven Facial Retouching Restoration

Due to the popularity of the Internet and smart mobile devices, more and more
financial transactions and activities have been digitalized. Compared to
traditional financial fraud detection strategies using credit-related features,
customers are generating a large amount of unstructured behavioral data every
second. In this paper, we propose an Recurrent Neural Netword (RNN) based
deep-learning structure integrated with Markov Transition Field (MTF) for
predicting online fraud behaviors using customer's interactions with websites
or smart-phone apps as a series of states. In practice, we tested and proved
that the proposed network structure for processing sequential behavioral data
could significantly boost fraud predictive ability comparing with the
multilayer perceptron network and distance based classifier with Dynamic Time
Warping(DTW) as distance metric.

本论文提出了基于 RNN 和 MTF 结构的深度学习框架，利用客户在网站或智能手机应用程序上的交互行为序列进行在线欺诈行为预测。实验结果表明，相较于多层感知机网络和动态时间规整距离法作为距离度量的距离分类器，该网络结构可显著提高欺诈预测性能。

使用深度学习和马尔可夫转移场进行的在线欺诈检测中的序列行为数据处理

Sequential Behavioral Data Processing Using Deep Learning and the Markov  Transition Field in Online Fraud Detection

As online fraudsters invest more resources, including purchasing large pools
of fake user accounts and dedicated IPs, fraudulent attacks become less obvious
and their detection becomes increasingly challenging. Existing approaches such
as average degree maximization suffer from the bias of including more nodes
than necessary, resulting in lower accuracy and increased need for manual
verification. Hence, we propose HoloScope, which uses information from graph
topology and temporal spikes to more accurately detect groups of fraudulent
users. In terms of graph topology, we introduce "contrast suspiciousness," a
dynamic weighting approach, which allows us to more accurately detect
fraudulent blocks, particularly low-density blocks. In terms of temporal
spikes, HoloScope takes into account the sudden bursts and drops of fraudsters'
attacking patterns. In addition, we provide theoretical bounds for how much
this increases the time cost needed for fraudsters to conduct adversarial
attacks. Additionally, from the perspective of ratings, HoloScope incorporates
the deviation of rating scores in order to catch fraudsters more accurately.
Moreover, HoloScope has a concise framework and sub-quadratic time complexity,
making the algorithm reproducible and scalable. Extensive experiments showed
that HoloScope achieved significant accuracy improvements on synthetic and real
data, compared with state-of-the-art fraud detection methods.

HoloScope 是一种新的在线欺诈检测算法，利用图形拓扑和时间峰值信息更准确地检测在线欺诈。它采用对比可疑度的权重方法，在图形拓扑和时间 spikes 方面考虑突发和下降欺诈攻击模式，同时考虑评分偏差和理论上限。该方法在合成和真实数据上表现出与现有欺诈检测方法相比显着的准确性提高。