The field of deepfake detection is moving towards more integrated and robust approaches, with a focus on effectively fusing spatial and temporal features to identify subtle and time-dependent manipulations. Researchers are exploring innovative methods, such as cross-attention mechanisms and language-driven segmental manipulation, to improve the accuracy and reliability of deepfake detection models. Noteworthy papers in this area include CAST, which leverages cross-attention to fuse spatial and temporal features, and PhonemeFake, which introduces a language-driven approach to manipulate critical speech segments. Other notable papers, such as CDAL and DDL, are making significant contributions to open-world model attribution and interpretable deepfake detection, respectively.