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：过去十年间，随着生成式人工智能（GenAI）的进步，合成指纹图像的质量和逼真度显著提升。这加剧了指纹识别系统对数据注入攻击的脆弱性——攻击者可能在注册或认证过程中恶意插入合成指纹。因此，亟需开发检测指纹图像真伪的方法。虽然训练深度神经网络（DNN）模型将图像分类为真实或合成相对简单，但这类DNN模型常会过拟合训练数据，当应用于未经训练的GenAI模型生成的合成指纹时，其泛化能力往往不足。本文将合成指纹检测形式化为持续小样本自适应问题，目标是快速演化基础检测器以识别新型合成数据。为实现持续小样本自适应，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），并在微调过程中重播少量已知风格的样本以缓解灾难性遗忘。基于多种DNN骨干网络（作为特征提取器）及真实与合成指纹数据集的实验表明，所提方法在快速适应检测未知合成风格与遗忘已知风格之间实现了良好平衡。