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。