PRIME: PROTOTYPE-ROUTED IMMUTABLE MONOTONE EXPERTS

In continual face anti-spoofing (FAS), a router sends each frame to an expert, but bonafide data from later sessions can stop it from sending a known attack to its expert. We propose Prototype-Routed Immutable Monotone Experts (PRIME) to keep these attack routes. When an attack class arrives, PRIME freezes its routing test and the bonafide evidence that the test uses, so later sessions cannot remove a route. We prove this property, conditional zero forgetting on attack routing (ZF-A). The routing test works in a fixed embedding space. PRIME learns this space with Joint Token Attention, a novel readout that weights every token of every layer of a frozen backbone. We pre-train this readout on FFHQ-SynthFAS, our new paired synthetic FAS corpus that we will release. We evaluate PRIME on two protocols. On OCIM leave-one-out with one source per session, PRIME has a mean HTER of 1.45% and outperforms the best continual (10.66%) and joint-training (1.82%) methods. It has the highest $mAA$ (99.97%) and $mAGA$ (98.63%), above joint training on the same backbone. On a chronological chain of three public datasets, PRIME has a mean HTER of 2.21% on five unseen datasets and outperforms training-free continual classifiers on the same features. They reverse 1.5% to 15.7% of attack decisions per session, but PRIME does not lose any route.