Face Anti-Spoofing (FAS) has become an important part of ensuring the reliability of biometric authentication systems. However, achieving unified detection of physical and digital attacks remains a serious challenge. Physical presentation attacks often introduce artifacts such as color distortion and moiré, while digital forgeries often tamper with facial images at the pixel level in an imperceptible way. To advance the development of this field, we released a massively expanded dataset, UniAttackData+, at the 6th Face Anti-Spoofing Workshop (ICCV 2025). The dataset covers 2,875 participants from three different ethnic groups (Africa, East Asia, and Central Asia), and a total of 18,250 real videos were collected under various lighting, background, and acquisition device conditions. For each participant, we designed and applied 54 attack methods (including 14 physical attacks and 40 digital attacks), generating a total of 679,097 forged videos, providing a rich, diverse, and challenging data resource for unified attack detection.