Image Prefiltering in DeepFake Detection

Abstract

Artificial intelligence, becoming common technology, creates a lot of new possibilities and dangers. An example can be open source applications that enable swapping faces on images or videos with other faces delivered from other sources. This type of modification is named DeepFake. Since the human eye cannot detect Deep-Fake, it is crucial to possess a mechanism that would detect such changes. This paper analyses solution based on Spatial Rich Models (SRM) for image prefiltering connecting convolutional neural network VGG16 to increase DeepFake detection with neural networks. For DeepFake detection, a fractional order spatial rich model (FoSRM) is proposed, which was compared with classical SRM filter and integer order derivative operators. In the experiment, we used two different approximation fractional order derivative methods: first based on the mask and second used Fast Fourier Transform (FFT). Achieved results we also compare with the original ones and the VGG16 network with an additional layer added to select the parameters of the prefiltering mask automatically. As a result of the work, we questioned the legitimacy of using additional image enrichment by prefiltering when using the convolutional neural network. Additional network layer gave us the best results from the performed experiments.