  Creating fake images and videos such as "Deepfake" has become much easier
these days due to the advancement in Generative Adversarial Networks (GANs).
Moreover, recent research such as the few-shot learning can create highly
realistic personalized fake images with only a few images. Therefore, the
threat of Deepfake to be used for a variety of malicious intents such as
propagating fake images and videos becomes prevalent. And detecting these
machine-generated fake images has been quite challenging than ever. In this
work, we propose a light-weight robust fine-tuning neural network-based
classifier architecture called Fake Detection Fine-tuning Network (FDFtNet),
which is capable of detecting many of the new fake face image generation
models, and can be easily combined with existing image classification networks
and finetuned on a few datasets. In contrast to many existing methods, our
approach aims to reuse popular pre-trained models with only a few images for
fine-tuning to effectively detect fake images. The core of our approach is to
introduce an image-based self-attention module called Fine-Tune Transformer
that uses only the attention module and the down-sampling layer. This module is
added to the pre-trained model and fine-tuned on a few data to search for new
sets of feature space to detect fake images. We experiment with our FDFtNet on
the GANsbased dataset (Progressive Growing GAN) and Deepfake-based dataset
(Deepfake and Face2Face) with a small input image resolution of 64x64 that
complicates detection. Our FDFtNet achieves an overall accuracy of 90.29% in
detecting fake images generated from the GANs-based dataset, outperforming the
state-of-the-art.
