  Due to the advancement of Generative Adversarial Networks (GAN),
Autoencoders, and other AI technologies, it has been much easier to create fake
images such as "Deepfakes". More recent research has introduced few-shot
learning, which uses a small amount of training data to produce fake images and
videos more effectively. Therefore, the ease of generating manipulated images
and the difficulty of distinguishing those images can cause a serious threat to
our society, such as propagating fake information. However, detecting realistic
fake images generated by the latest AI technology is challenging due to the
reasons mentioned above. In this work, we propose Dual Attention Fake Detection
Fine-tuning Network (DA-FDFtNet) to detect the manipulated fake face images
from the real face data. Our DA-FDFtNet integrates the pre-trained model with
Fine-Tune Transformer, MBblockV3, and a channel attention module to improve the
performance and robustness across different types of fake images. In
particular, Fine-Tune Transformer consists of multiple numbers of an
image-based self-attention module and a down-sampling layer. The channel
attention module is also connected with the pre-trained model to capture the
fake images feature space. We experiment with our DA-FDFtNet with the
FaceForensics++ dataset and various GAN-generated datasets, and we show that
our approach outperforms the previous baseline models.
