Fake Image Detection Using An Ensemble of CNN Models Specialized For Individual Face Parts
Akihisa Kawabe, Ryuto Haga, Yoichi Tomioka, Yuichi Okuyama, Jungpil Shin · 2022
With the rapid increase of deep learning technology, creating human face images with artificial intelligence (AI) is becoming easier. Those generated images are coming up to images that humans cannot distinguish from authentic ones. It is essential to realize an accurate method to detect such fake images to avoid abusing them. In this paper, we propose a fake image detection using an ensemble model of convolutional neural network (CNN) models that focus on deepfake detection of individual face parts. Our results show that a combination of deepfake detection based on different face parts is effective. This idea can be adopted on partially manipulated deepfake images/videos.