TSANet: Unified Face Attack Detection via Triplet Embedding and Soft ACER Loss

Face-based authentication technologies are increasingly adopted in identity verification systems due to their convenience and ease of use. However, they remain vulnerable to a wide range of spoofing attacks. To address this, we propose TSANet, a unified and robust detection framework that combines contrastive feature learning with a probabilistic decision model. Unlike many existing methods that d…