The field of deepfake detection and face recognition is rapidly advancing, with a focus on improving the robustness and generalizability of detection models. Recent research has explored the use of novel approaches, such as leveraging intermediate features of vision transformers, multimodal datasets, and reconstruction-based methods, to detect and prevent deepfake attacks. Additionally, there is a growing interest in developing more effective out-of-distribution detection strategies and improving the adversarial robustness of AI-generated image detectors. Noteworthy papers include Logits-Based Finetuning, which proposes a reconstruction-based method for out-of-distribution detection, and AuthGuard, which incorporates language guidance to improve deepfake detection generalization. Overall, the field is moving towards more robust and adaptable solutions to counter the evolving threat of deepfakes and improve face recognition systems.