A deep learning approach to face swap detection

Svetlana Volkova, Alexey Bogdanov


We propose the method for detecting an incident at face authentication when an imposter falsifies the client's face using a digital technique named Face Swap to cheat the system. The method is based on a convolutional neural network to get facial features and classify them. The proposed method can work with faces obtained with low quality and heavy lighting conditions. It is confirmed by experiments on a big test dataset. Experiments show that the accuracy reaches values over 98% for low-quality images and over 99% for high-quality images. Classification results are congruent to the best results shown by the other known methods tested on the same test dataset. The proposed method can be applied to improve the quality of face authentication systems.

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