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.

This paper introduces CogniSNN, a novel Spiking Neural Network (SNN) paradigm that incorporates Random Graph Architectures (RGA) to address the limitations of traditional, rigid SNN designs. CogniSNN enhances neuron-expandability, pathway-reusability, and dynamic-configurability, leading to improved performance, robustness, and continual learning capabilities in multi-task scenarios.