The rapid development in the field of deep learning has led to the general adoption of the deepfake concept. Deepfakes are synthetic media that are often created maliciously and therefore pose an increasingly significant challenge to modern society. For this reason, it is crucial to develop robust and effective methods for detecting deepfakes to prevent their malicious use. In our work, we implemented the Xception convolutional neural network, which we upgraded with an architecture that operates on the principle of two separate learning branches. The first branch learns only on the manipulated facial region, while the second branch uses non-manipulated regions outside of the face region to predict the final result. The implemented dual-branch architecture improves the performance of the baseline Xception model by 2.45 % in terms of AUC value from the original value of 69.76 %. We additionally trained the implemented models on a newly created synthetic dataset of deepfake artifacts, where the Xception model achieves a 12.5 % improvement in the AUC value of the baseline model with the value of 69.76 %
|
