In recent years, deep learning has greatly streamlined the process of
generating realistic fake face images. Aware of the dangers, researchers have
developed various tools to spot these counterfeits. Yet none asked the
fundamental question: What digital manipulations make a real photographic face
image fake, while others do not? In this paper, we put face forgery in a
semantic context and define that computational methods that alter semantic face
attributes to exceed human discrimination thresholds are sources of face
forgery. Guided by our new definition, we construct a large face forgery image
dataset, where each image is associated with a set of labels organized in a
hierarchical graph. Our dataset enables two new testing protocols to probe the
generalization of face forgery detectors. Moreover, we propose a
semantics-oriented face forgery detection method that captures label relations
and prioritizes the primary task (\ie, real or fake face detection). We show
that the proposed dataset successfully exposes the weaknesses of current
detectors as the test set and consistently improves their generalizability as
the training set. Additionally, we demonstrate the superiority of our
semantics-oriented method over traditional binary and multi-class
classification-based detectors.

通过定义改变语义脸部属性以超过人类辨别阈值的计算方法，构建一个大型脸部伪造图像数据集，并提出了一个以语义为导向的脸部伪造检测方法，展示了该数据集在揭示当前检测器的弱点以及提高其泛化能力方面的优越性。

人脸伪造的语义上下文化：新的定义、数据集和检测方法

Semantic Contextualization of Face Forgery: A New Definition, Dataset,  and Detection Method

The rapid progression of Generative Adversarial Networks (GANs) has raised a
concern of their misuse for malicious purposes, especially in creating fake
face images. Although many proposed methods succeed in detecting GAN-based
synthetic images, they are still limited by the need for large quantities of
the training fake image dataset and challenges for the detector's
generalizability to unknown facial images. In this paper, we propose a new
approach that explores the asynchronous frequency spectra of color channels,
which is simple but effective for training both unsupervised and supervised
learning models to distinguish GAN-based synthetic images. We further
investigate the transferability of a training model that learns from our
suggested features in one source domain and validates on another target domains
with prior knowledge of the features' distribution. Our experimental results
show that the discrepancy of spectra in the frequency domain is a practical
artifact to effectively detect various types of GAN-based generated images.

本文提出了一种基于异步频率谱的方法，对生成对抗网络（GAN）的合成图像进行识别和检测，并在不同领域之间进行了验证。

探讨生成对抗网络产生的面部图像频谱的异步性

Exploring the Asynchronous of the Frequency Spectra of GAN-generated  Facial Images

Images synthesized by powerful generative adversarial network (GAN) based
methods have drawn moral and privacy concerns. Although image forensic models
have reached great performance in detecting fake images from real ones, these
models can be easily fooled with a simple adversarial attack. But, the noise
adding adversarial samples are also arousing suspicion. In this paper, instead
of adding adversarial noise, we optimally search adversarial points on face
manifold to generate anti-forensic fake face images. We iteratively do a
gradient-descent with each small step in the latent space of a generative
model, e.g. Style-GAN, to find an adversarial latent vector, which is similar
to norm-based adversarial attack but in latent space. Then, the generated fake
images driven by the adversarial latent vectors with the help of GANs can
defeat main-stream forensic models. For examples, they make the accuracy of
deepfake detection models based on Xception or EfficientNet drop from over 90%
to nearly 0%, meanwhile maintaining high visual quality. In addition, we find
manipulating style vector $z$ or noise vectors $n$ at different levels have
impacts on attack success rate. The generated adversarial images mainly have
facial texture or face attributes changing.

本研究提出一种新的反 - 取证攻击方法，该方法利用梯度下降算法在 Style-GAN 的潜空间中搜索对抗性潜向量，生成高质量的假人脸图像，并能够成功地欺骗主流取证模型，从而达到反 - 取证的目的。