Robust DeepFake Detection Method based on Ensemble of ViT and CNN
H Nguyen Ha, Minsang Kim, Songhun Han, Sangjun Lee · 2023
With the development of convolutional neural networks (CNN) and generative adversarial networks (GAN) in recent years, classifying fake videos produced through DeepFake has become a very difficult task. Most previous studies on DeepFake Detection were focused on finding DeepFake artifacts through CNN. DeepFake detection using CNN has high accuracy, but is vulnerable to noisy inputs such as side faces, shadowed faces, and low-quality images. In addition, although it has the advantage of being able to learn quickly through inductive bias, it tends to be overfitted to specific datasets, showing low accuracy in manipulated videos created with a different type of DeepFake from training datasets.