Accuracy and Robustness of State of the Art Deepfake Detection Models

Sammanfattning: With the evolution of artificial intelligence a lot of people have started getting worried about the potential dangers of deepfake images and videos, such as spreading fake videos of influential people. Several solutions to this problem have been proposed with some of the most efficient being convolutional neural networks for face detection in order to differentiate real images from deepfake images generated with a generative adversarial network. One of the currently most prevalent models is the VGGFace which is further analyzed in the report. This project explores how different hyperparameters affect the effectiveness of existing convolutional neuralnetworks aswell as the robustness in the models. The hyper-parameter that had the biggest effect on accuracy was the amount of conovultion layers in each step of the network. The results showed that while deepfake detection models showed high accuracy on the test set, they are lackluster when it comes to the robustness. The models showed a clear sensitivity for the resolution of test images. This is an issue that can be solved through resizing, this report shows the more concerning issue where the model had a 47 percentage point reduction in accuracy when tested on a different dataset that had fake images generated with a different generative adversarial network. The main takeaways from the project is that current deepfake detection models have to work on generalization in order to effectively classify images.