The field of deepfake detection is rapidly advancing, with a focus on developing more effective and generalizable methods for detecting synthetic media. Researchers are exploring new approaches, such as multimodal learning and variational Bayesian estimation, to improve the accuracy and robustness of deepfake detection models. One notable trend is the integration of audio-visual correlation learning, which exposes subtle cross-modal inconsistencies that can serve as crucial clues in deepfake detection. Another area of research is the development of more comprehensive benchmarks, such as AVFakeBench, which spans rich forgery semantics across both human subject and general subject. Noteworthy papers include: UMCL, which achieves superior performance across various compression rates and manipulation types, and AuViRe, which reconstructs speech representations from one modality based on the other, providing robust discriminative cues for precise temporal forgery localization. FoVB, a variational Bayesian approach, also demonstrates state-of-the-art performance in deepfake detection, and AVFakeBench establishes a multi-task evaluation framework for audio-video forgery detection.