Deepfakes have rapidly emerged as a profound and serious threat to society,
primarily due to their ease of creation and dissemination. This situation has
triggered an accelerated development of deepfake detection technologies.
However, many existing detectors rely heavily on lab-generated datasets for
validation, which may not effectively prepare them for novel, emerging, and
real-world deepfake techniques. In this paper, we conduct an extensive and
comprehensive review and analysis of the latest state-of-the-art deepfake
detectors, evaluating them against several critical criteria. These criteria
facilitate the categorization of these detectors into 4 high-level groups and
13 fine-grained sub-groups, all aligned with a unified standard conceptual
framework. This classification and framework offer deep and practical insights
into the factors that affect detector efficacy. We assess the generalizability
of 16 leading detectors across various standard attack scenarios, including
black-box, white-box, and gray-box settings. Our systematized analysis and
experimentation lay the groundwork for a deeper understanding of deepfake
detectors and their generalizability, paving the way for future research
focused on creating detectors adept at countering various attack scenarios.
Additionally, this work offers insights for developing more proactive defenses
against deepfakes.

本研究对最新的深度伪造检测器进行了广泛综述和分析，评估了它们在几个关键标准下的表现，并将它们分类成四个高水平组和十三个细粒度子组，进一步提供了关于影响检测器有效性的因素的深入实际见解。通过考察 16 个主要检测器在多种标准攻击场景下的普适性，包括黑盒、白盒和灰盒设置，系统性的分析和实验为对抗各种攻击场景的深度伪造检测器的研究奠定了基础，并为制定更积极的防御策略提供了启示。

面部深度伪造检测器概述

SoK: Facial Deepfake Detectors

Over the past decade, there has been tremendous progress in creating
synthetic media, mainly thanks to the development of powerful methods based on
generative adversarial networks (GAN). Very recently, methods based on
diffusion models (DM) have been gaining the spotlight. In addition to providing
an impressive level of photorealism, they enable the creation of text-based
visual content, opening up new and exciting opportunities in many different
application fields, from arts to video games. On the other hand, this property
is an additional asset in the hands of malicious users, who can generate and
distribute fake media perfectly adapted to their attacks, posing new challenges
to the media forensic community. With this work, we seek to understand how
difficult it is to distinguish synthetic images generated by diffusion models
from pristine ones and whether current state-of-the-art detectors are suitable
for the task. To this end, first we expose the forensics traces left by
diffusion models, then study how current detectors, developed for GAN-generated
images, perform on these new synthetic images, especially in challenging
social-networks scenarios involving image compression and resizing. Datasets
and code are available at github.com/grip-unina/DMimageDetection.

本文旨在了解人们无法将由扩散模型产生的合成图像与原始图像区分开来的难度，并研究当前的最先进检测器是否适用于此任务。

关于检测扩散模型生成的合成图像

On the detection of synthetic images generated by diffusion models

In existing CNN based detectors, the backbone network is a very important
component for basic feature extraction, and the performance of the detectors
highly depends on it. In this paper, we aim to achieve better detection
performance by building a more powerful backbone from existing backbones like
ResNet and ResNeXt. Specifically, we propose a novel strategy for assembling
multiple identical backbones by composite connections between the adjacent
backbones, to form a more powerful backbone named Composite Backbone Network
(CBNet). In this way, CBNet iteratively feeds the output features of the
previous backbone, namely high-level features, as part of input features to the
succeeding backbone, in a stage-by-stage fashion, and finally the feature maps
of the last backbone (named Lead Backbone) are used for object detection. We
show that CBNet can be very easily integrated into most state-of-the-art
detectors and significantly improve their performances. For example, it boosts
the mAP of FPN, Mask R-CNN and Cascade R-CNN on the COCO dataset by about 1.5
to 3.0 percent. Meanwhile, experimental results show that the instance
segmentation results can also be improved. Specially, by simply integrating the
proposed CBNet into the baseline detector Cascade Mask R-CNN, we achieve a new
state-of-the-art result on COCO dataset (mAP of 53.3) with single model, which
demonstrates great effectiveness of the proposed CBNet architecture. Code will
be made available on this https URL

本文提出了一种名为 CBNet 的复合骨干网络，通过组合相邻骨干的复合连接来形成更强大的骨干，可以显著提高基于 CNN 的检测器的性能，并且可以轻松地整合到大多数最先进的检测器中并显著提高它们的性能。