This paper introduces a novel approach for detecting synthetic fingerprints, framing it as a continual few-shot adaptation problem. It proposes using a combination of binary cross-entropy and supervised contrastive losses with experience replay to enable rapid adaptation to new synthetic data styles while mitigating catastrophic forgetting.