This survey addresses the critical challenge of deepfake detection amidst the
rapid advancements in artificial intelligence. As AI-generated media, including
video, audio and text, become more realistic, the risk of misuse to spread
misinformation and commit identity fraud increases. Focused on face-centric
deepfakes, this work traces the evolution from traditional single-modality
methods to sophisticated multi-modal approaches that handle audio-visual and
text-visual scenarios. We provide comprehensive taxonomies of detection
techniques, discuss the evolution of generative methods from auto-encoders and
GANs to diffusion models, and categorize these technologies by their unique
attributes. To our knowledge, this is the first survey of its kind. We also
explore the challenges of adapting detection methods to new generative models
and enhancing the reliability and robustness of deepfake detectors, proposing
directions for future research. This survey offers a detailed roadmap for
researchers, supporting the development of technologies to counter the
deceptive use of AI in media creation, particularly facial forgery. A curated
list of all related papers can be found at
\href{https://github.com/qiqitao77/Comprehensive-Advances-in-Deepfake-Detection-Spanning-Diverse-Modalities}{this https URL}.

通过对深度伪造检测方法的综述研究，本论文提供了面向人脸为中心的深度伪造技术的分类、检测方法的演化以及适应新的生成模型的挑战，并探索了提高深度伪造检测器的可靠性和稳健性的方向。