LAUSR

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.