Abstract

The identification of attempts to substitute images plays an important role in protecting biometric systems (authorization in mobile devices, access control systems for premises, terminals with automatic access by face recognition, etc.). This study presents a new method for detecting falsified images based on processing the multimodal data from a camera. A new neural network architecture is developed that aggregates the features from different modalities at all levels of the model. The separation of the training sample for different types of attacks and the initialization of the model with attributes trained in other tasks that are associated with facial images are considered. Numerical experiments on real data are performed, showing the successful performance of the system. The proposed model won first place in the CASIA-SURF competition for the recognition of falsified facial images.

中文翻译：

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训练多模态神经网络以确定图像的真实性

摘要

尝试替换图像的标识在保护生物识别系统（移动设备中的授权，房屋的访问控制系统，具有通过面部识别自动访问的终端等）中起着重要的作用。这项研究提出了一种基于处理来自相机的多峰数据来检测伪造图像的新方法。开发了一种新的神经网络架构，该架构在模型的各个级别汇总了来自不同模态的特征。考虑了针对不同类型攻击的训练样本的分离以及具有在与面部图像相关联的其他任务中训练的属性的模型的初始化。对真实数据进行了数值实验，显示了该系统的成功性能。