2024/04/19 by Santosh, Lin Li, Irene Amerini +6 · 7 citations
Computer Science · Engineering · Medicine · #Image and Signal Denoising Methods #Image Processing Techniques and Applications #Medical Imaging Techniques and Applications
paper · pdf · doi:10.48550/arxiv.2404.12908
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/RobustDMGeneratedImageDetection.