An Efficient Frequency Domain Based Attribution and Detection Network

People nowadays can easily synthesize high fidelity fake images with different types of image content due to the rapid advances of deep learning technologies. Detecting such images and attributing them to their generative models (GMs) is crucial. Existing deep learning methods attempt to identify and classify GM-specific artifacts but often struggle with content-independence and generalizability. In this paper, we observe that while GMs leave unique artifacts in the frequency domain, they are coupled with the image content. Based on this observation, we propose a novel deep learning-based solution that learns input-adaptive masks to highlight GMs’ artifacts and achieve high accuracy on the synthesized image attribution task. In addition, we observed that GMs’ artifacts in the frequency domain remain intact in sub-images of the original image, and they are even retained when the images are distorted. To further improve the accuracy of the proposed solution, we leverage the characteristics of GMs artifacts in sub-images and distorted images to make our network perform more effectively. Our evaluation results show that our proposed solution outperforms other state-of-the-art methods on unseen image types, showing great generalizability.