The growing diversity of digital face manipulation techniques has led to an
urgent need for a universal and robust detection technology to mitigate the
risks posed by malicious forgeries. We present a blended-based detection
approach that has robust applicability to unseen datasets. It combines a method
for generating synthetic training samples, i.e., reconstructed blended images,
that incorporate potential deepfake generator artifacts and a detection model,
a multi-scale feature reconstruction network, for capturing the generic
boundary artifacts and noise distribution anomalies brought about by digital
face manipulations. Experiments demonstrated that this approach results in
better performance in both cross-manipulation detection and cross-dataset
detection on unseen data.

通过生成合成的混合图像并利用多尺度特征重建网络，实现更好的跨操作检测和跨数据集检测性能。

利用再构和混合图像以及多尺度特征再构神经网络进行广义深度伪造检测

Generalized Deepfakes Detection with Reconstructed-Blended Images and  Multi-scale Feature Reconstruction Network

Detecting digital face manipulation has attracted extensive attention due to
fake media's potential harms to the public. However, recent advances have been
able to reduce the forgery signals to a low magnitude. Decomposition, which
reversibly decomposes an image into several constituent elements, is a
promising way to highlight the hidden forgery details. In this paper, we
consider a face image as the production of the intervention of the underlying
3D geometry and the lighting environment, and decompose it in a computer
graphics view. Specifically, by disentangling the face image into 3D shape,
common texture, identity texture, ambient light, and direct light, we find the
devil lies in the direct light and the identity texture. Based on this
observation, we propose to utilize facial detail, which is the combination of
direct light and identity texture, as the clue to detect the subtle forgery
patterns. Besides, we highlight the manipulated region with a supervised
attention mechanism and introduce a two-stream structure to exploit both face
image and facial detail together as a multi-modality task. Extensive
experiments indicate the effectiveness of the extra features extracted from the
facial detail, and our method achieves the state-of-the-art performance.

本文提出了一种将人脸图像分解为 3D 几何形状、贴图和光照的计算机视图，使用人脸细节结合直接光和身份贴图来检测微妙的篡改模式并实现了最先进的性能表现。

通过 3D 分解进行人脸伪造检测

Face Forgery Detection by 3D Decomposition

Digital face manipulation has become a popular and fascinating way to touch
images with the prevalence of smartphones and social networks. With a wide
variety of user preferences, facial expressions, and accessories, a general and
flexible model is necessary to accommodate different types of facial editing.
In this paper, we propose a model to achieve this goal based on an end-to-end
convolutional neural network that supports fast inference, edit-effect control,
and quick partial-model update. In addition, this model learns from unpaired
image sets with different attributes. Experimental results show that our
framework can handle a wide range of expressions, accessories, and makeup
effects. It produces high-resolution and high-quality results in fast speed.

通过卷积神经网络，本篇论文提出了一个支持快速推理、快速部分模型更新、不配对图像集学习的模型来实现数字人脸编辑的目标。实验结果表明：该模型可以处理各种表情、装饰和化妆效果，快速生成高分辨率和高质量的数字人脸编辑图像。