The field of audio and video deepfake detection is witnessing significant advancements, particularly in addressing the evolving nature of synthetic content. Researchers are focusing on developing more versatile and adaptive detection methods that can handle the increasing complexity and diversity of deepfake techniques. Key innovations include the use of continual learning strategies to adapt models to new types of deepfakes, the integration of transformer-based architectures for universal detection across various manipulation types, and the exploration of phoneme-level feature discrepancies to enhance speech deepfake detection. Additionally, there is a growing emphasis on multimodal coherence analysis to detect inconsistencies in talking face generation. These developments not only improve the accuracy and robustness of detection methods but also broaden their applicability to a wider range of synthetic content scenarios. Notably, the introduction of new datasets and frameworks is filling critical gaps in the research, enabling more comprehensive evaluations and advancements in the field.