Young Oh Bang

Young Oh Bang, Simon S. Woo

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.

Hyeonseong Jeon, Youngoh Bang, Junyaup Kim et al.

Recent advancements in Generative Adversarial Networks (GANs) enable the generation of highly realistic images, raising concerns about their misuse for malicious purposes. Detecting these GAN-generated images (GAN-images) becomes increasingly challenging due to the significant reduction of underlying artifacts and specific patterns. The absence of such traces can hinder detection algorithms from identifying GAN-images and transferring knowledge to identify other types of GAN-images as well. In this work, we present the Transferable GAN-images Detection framework T-GD, a robust transferable framework for an effective detection of GAN-images. T-GD is composed of a teacher and a student model that can iteratively teach and evaluate each other to improve the detection performance. First, we train the teacher model on the source dataset and use it as a starting point for learning the target dataset. To train the student model, we inject noise by mixing up the source and target datasets, while constraining the weight variation to preserve the starting point. Our approach is a self-training method, but distinguishes itself from prior approaches by focusing on improving the transferability of GAN-image detection. T-GD achieves high performance on the source dataset by overcoming catastrophic forgetting and effectively detecting state-of-the-art GAN-images with only a small volume of data without any metadata information.