GRE-Net: A forgery image detection framework based on gradient feature and reconstruction error

Abstract

With the continuous technological breakthroughs in Generative Adversarial Networks (GANs) and diffusion models, remarkable progress has been achieved in the field of image generation. These technologies enable the creation of highly realistic images, thereby intensifying the risk of spreading fake information. However, traditional image detectors face a growing challenge of inadequate generalization capabilities when confronted with images generated by models that were not included during the training phase. To tackle this challenge, we introduce a novel detection framework, named GRE-Net (Network integrating Gradient and Reconstruction Error), which extracts gradient feature through the DPG module and calculates the reconstruction error utilizing the DIRE method. By integrating these two aspects into a comprehensive feature representation, GRE-Net effectively detects the authenticity of images. Specifically, we devise a dual-branch model that leverages the proposed DPG (Discriminator of ProjectedGAN to extract Gradient) module to extract gradient feature from images and concurrently employs the DIRE (DIffusion Reconstruction Error) method to obtain the diffusion reconstruction error of images. By fusing the features extracted from these two modules as a universal representation, we describe the artifacts produced by generative models, crafting a comprehensive detector capable of identifying both GAN-generated and diffusion model-generated images. Notably, the DPG approach utilizes the discriminator of ProjectedGAN as an intermediary bridge, mapping all data into the gradient domain. This transformation process effectively captures the intrinsic feature differences during the image generation process. Subsequently, the gradient feature are fed into a classifier to achieve efficient discrimination between authentic and fake images. To validate the efficacy of our proposed detector, we conducted evaluations on a dataset comprising images generated by ten diverse diffusion models and GANs. Extensive experiments demonstrate that our detector exhibits stronger generalization capabilities and higher robustness, rendering it suitable for real-world generated image detection tasks.