Advancements like Generative Adversarial Networks have attracted the
attention of researchers toward face image synthesis to generate ever more
realistic images. Thereby, the need for the evaluation criteria to assess the
realism of the generated images has become apparent. While FID utilized with
InceptionV3 is one of the primary choices for benchmarking, concerns about
InceptionV3's limitations for face images have emerged. This study investigates
the behavior of diverse feature extractors -- InceptionV3, CLIP, DINOv2, and
ArcFace -- considering a variety of metrics -- FID, KID, Precision\&Recall.
While the FFHQ dataset is used as the target domain, as the source domains, the
CelebA-HQ dataset and the synthetic datasets generated using StyleGAN2 and
Projected FastGAN are used. Experiments include deep-down analysis of the
features: $L_2$ normalization, model attention during extraction, and domain
distributions in the feature space. We aim to give valuable insights into the
behavior of feature extractors for evaluating face image synthesis
methodologies. The code is publicly available at
this https URL

对于评估人脸图像生成的真实性，研究人员关注生成对抗网络等新技术的进展，本研究通过调查不同特征提取器（InceptionV3、CLIP、DINOv2 和 ArcFace）的行为，考虑多种指标（FID、KID、Precision＆Recall）来评估生成图像的真实性，并对特征的深入分析包括 L2 归一化、模型关注机制和特征空间中的数据分布，旨在为评估人脸图像生成方法提供有价值的见解。

面部图像合成的特征提取网络分析

Analyzing the Feature Extractor Networks for Face Image Synthesis

Face image synthesis is gaining more attention in computer security due to
concerns about its potential negative impacts, including those related to fake
biometrics. Hence, building models that can detect the synthesized face images
is an important challenge to tackle. In this paper, we propose a fusion-based
strategy to detect face image synthesis while providing resiliency to several
attacks. The proposed strategy uses a late fusion of the outputs computed by
several undisclosed models by relying on random polynomial coefficients and
exponents to conceal a new feature space. Unlike existing concealing solutions,
our strategy requires no quantization, which helps to preserve the feature
space. Our experiments reveal that our strategy achieves state-of-the-art
performance while providing protection against poisoning, perturbation,
backdoor, and reverse model attacks.

提出了一种基于融合的策略来检测面部图像合成，同时提供对多种攻击的弹性保护。该策略通过使用多个未公开的模型计算的输出的后期融合，依靠随机多项式系数和指数来隐藏新的特征空间，而无需量化，从而有助于保留特征空间，并在抵御污染、扰动、后门和反向模型攻击方面达到最先进的性能。

利用隐蔽的融合模型检测人脸合成

Detecting Face Synthesis Using a Concealed Fusion Model

Recent advances in generative adversarial networks (GANs) have led to
remarkable achievements in face image synthesis. While methods that use
style-based GANs can generate strikingly photorealistic face images, it is
often difficult to control the characteristics of the generated faces in a
meaningful and disentangled way. Prior approaches aim to achieve such semantic
control and disentanglement within the latent space of a previously trained
GAN. In contrast, we propose a framework that a priori models physical
attributes of the face such as 3D shape, albedo, pose, and lighting explicitly,
thus providing disentanglement by design. Our method, MOST-GAN, integrates the
expressive power and photorealism of style-based GANs with the physical
disentanglement and flexibility of nonlinear 3D morphable models, which we
couple with a state-of-the-art 2D hair manipulation network. MOST-GAN achieves
photorealistic manipulation of portrait images with fully disentangled 3D
control over their physical attributes, enabling extreme manipulation of
lighting, facial expression, and pose variations up to full profile view.

MOST-GAN 使用 3D 形态模型耦合现代化 2D 变形网络实现面部图像生成、控制和逼真操作。

MOST-GAN: 用于解耦脸部图像操作的 3D 可塑造风格 GAN

MOST-GAN: 3D Morphable StyleGAN for Disentangled Face Image Manipulation

The advance of Generative Adversarial Networks (GANs) enables realistic face
image synthesis. However, synthesizing face images that preserve facial
identity as well as have high diversity within each identity remains
challenging. To address this problem, we present FaceFeat-GAN, a novel
generative model that improves both image quality and diversity by using two
stages. Unlike existing single-stage models that map random noise to image
directly, our two-stage synthesis includes the first stage of diverse feature
generation and the second stage of feature-to-image rendering. The competitions
between generators and discriminators are carefully designed in both stages
with different objective functions. Specially, in the first stage, they compete
in the feature domain to synthesize various facial features rather than images.
In the second stage, they compete in the image domain to render photo-realistic
images that contain high diversity but preserve identity. Extensive experiments
show that FaceFeat-GAN generates images that not only retain identity
information but also have high diversity and quality, significantly
outperforming previous methods.

本文提出了 FaceFeat-GAN 模型，使用两阶段的模型合成人脸图像，通过在不同阶段的目标函数精心设计，能够在保留身份信息的同时生成高多样性和高质量的图像，较之前的方法表现显著优越。