The field of face anti-spoofing and attack detection is rapidly evolving, with a focus on developing more robust and generalizable methods to counter various types of attacks. Recent research has emphasized the importance of effectively modeling long-range dependencies and locally descriptive features to improve the characterization of live and spoof faces. Noteworthy papers in this area include Enhancing Learnable Descriptive Convolutional Vision Transformer for Face Anti-Spoofing, which proposes novel training strategies to boost feature characterization capability, and Unsupervised Feature Disentanglement and Augmentation Network for One-class Face Anti-spoofing, which enhances generalizability by augmenting face images via disentangled features. Additionally, FA^3-CLIP and Mixture-of-Attack-Experts with Class Regularization for Unified Physical-Digital Face Attack Detection introduce innovative approaches to unified attack detection, leveraging frequency-aware cues fusion, attack-agnostic prompt learning, and class-aware regularization. Lastly, GRU-AUNet presents a domain adaptation framework for contactless fingerprint presentation attack detection, demonstrating robust resilience against presentation attacks.