Continual Few-shot Adaptation for Synthetic Fingerprint Detection

2026-03-15

AI Summary

This work addresses the poor generalization of existing detection models against unseen synthetic fingerprints generated by generative AI and their susceptibility to catastrophic forgetting. It formulates synthetic fingerprint detection as a continual few-shot adaptation problem for the first time, proposing a fine-tuning strategy that combines binary cross-entropy with supervised contrastive loss. To mitigate forgetting, the method incorporates an experience replay mechanism using a small set of samples from previously encountered fingerprint styles. Evaluated across multiple real and synthetic fingerprint datasets, the approach significantly enhances rapid adaptation to novel synthetic styles while effectively preserving knowledge of known styles, thereby achieving a balanced trade-off between memory retention and adaptability.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Life-Long and Continual Learning
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

The quality and realism of synthetically generated fingerprint images have increased significantly over the past decade fueled by advancements in generative artificial intelligence (GenAI). This has exacerbated the vulnerability of fingerprint recognition systems to data injection attacks, where synthetic fingerprints are maliciously inserted during enrollment or authentication. Hence, there is an urgent need for methods to detect if a fingerprint image is real or synthetic. While it is straightforward to train deep neural network (DNN) models to classify images as real or synthetic, often such DNN models overfit the training data and fail to generalize well when applied to synthetic fingerprints generated using unseen GenAI models. In this work, we formulate synthetic fingerprint detection as a continual few-shot adaptation problem, where the objective is to rapidly evolve a base detector to identify new types of synthetic data. To enable continual few-shot adaptation, we employ a combination of binary cross-entropy and supervised contrastive (applied to the feature representation) losses and replay a few samples from previously known styles during fine-tuning to mitigate catastrophic forgetting. Experiments based on several DNN backbones (as feature extractors) and a variety of real and synthetic fingerprint datasets indicate that the proposed approach achieves a good trade-off between fast adaptation for detecting unseen synthetic styles and forgetting of known styles.

Problem

• synthetic fingerprint detection
• continual adaptation
• few-shot learning
• generative AI
• data injection attacks

Innovation

• continual few-shot adaptation
• synthetic fingerprint detection
• supervised contrastive loss
• catastrophic forgetting mitigation
• generative AI security