Morphing attacks keep threatening biometric systems, especially face recognition systems. Over time they have become simpler to perform and more realistic, as such, the usage of deep learning systems to detect these attacks has grown. At the same time, there is a constant concern regarding the lack of interpretability of deep learning models. Balancing performance and interpretability has been a difficult task for scientists. However, by leveraging domain information and proving some constraints, we have been able to develop IDistill, an interpretable method with state-of-the-art performance that provides information on both the identity separation on morph samples and their contribution to the final prediction. The domain information is learnt by an autoencoder and distilled to a classifier system in order to teach it to separate identity information. When compared to other methods in the literature it outperforms them in three out of five databases and is competitive in the remaining.

翻译：融合攻击持续威胁生物识别系统，尤其是人脸识别系统。随着时间推移，此类攻击的实施方式愈发简便且更加逼真，因此利用深度学习系统检测这类攻击的需求日益增长。同时，深度学习模型可解释性不足的问题始终令人担忧。如何在性能与可解释性之间取得平衡一直是科学家面临的难题。然而，通过利用领域信息并施加特定约束，我们成功开发出IDistill——一种兼具前沿性能与可解释性的方法，能够提供融合样本中身份分离程度及其对最终预测贡献的双重信息。该方法通过自编码器学习领域特征，并将知识蒸馏至分类器系统，以训练其分离身份信息的能力。与文献中的其他方法相比，IDistill在五个数据库中的三个表现更优，在其余两个中具有竞争性。