  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.
