  Diffusion models (DMs) have revolutionized image generation, producing
high-quality images with applications spanning various fields. However, their
ability to create hyper-realistic images poses significant challenges in
distinguishing between real and synthetic content, raising concerns about
digital authenticity and potential misuse in creating deepfakes. This work
introduces a robust detection framework that integrates image and text features
extracted by CLIP model with a Multilayer Perceptron (MLP) classifier. We
propose a novel loss that can improve the detector's robustness and handle
imbalanced datasets. Additionally, we flatten the loss landscape during the
model training to improve the detector's generalization capabilities. The
effectiveness of our method, which outperforms traditional detection
techniques, is demonstrated through extensive experiments, underscoring its
potential to set a new state-of-the-art approach in DM-generated image
detection. The code is available at
https://github.com/Purdue-M2/Robust_DM_Generated_Image_Detection.
