Face Spoofing

Face spoofing, encompassing both presentation attacks (e.g., using photos or masks to fool facial recognition) and digital forgeries (e.g., deepfakes), aims to develop robust methods for distinguishing genuine faces from fraudulent ones. Current research emphasizes improving the generalization capabilities of anti-spoofing systems across diverse attack types and domains, employing techniques like generative diffusion models, vision transformers, and multimodal large language models to analyze visual and physiological cues (e.g., heart rate). This field is crucial for securing biometric authentication systems and combating the spread of misinformation, with ongoing efforts focused on developing more accurate, explainable, and computationally efficient solutions.