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.

翻译：在过去十年中，得益于生成式人工智能（GenAI）的进步，合成生成的指纹图像的质量和真实感显著提高。这加剧了指纹识别系统对数据注入攻击的脆弱性，即合成指纹在注册或认证过程中被恶意插入。因此，迫切需要能够检测指纹图像是真实还是合成的方法。虽然训练深度神经网络（DNN）模型将图像分类为真实或合成是直接的，但此类DNN模型常常会过拟合训练数据，并且在应用于使用未见过的GenAI模型生成的合成指纹时，泛化能力不佳。在这项工作中，我们将合成指纹检测表述为一个持续少样本适应问题，其目标是快速进化一个基础检测器以识别新型合成数据。为了实现持续少样本适应，我们结合使用二元交叉熵损失和监督对比损失（应用于特征表示），并在微调期间重放少量先前已知风格的样本，以减轻灾难性遗忘。基于多种DNN骨干网络（作为特征提取器）以及各种真实和合成指纹数据集的实验表明，所提出的方法在快速适应检测未见过的合成风格与遗忘已知风格之间取得了良好的平衡。