DiffCoR: Exposing AI-Generated Image by Using Stable Diffusion Model Based on Consistent Representation Learning

Abstract

Diffusion-based generative models have significantly advanced the field of image synthesis, presenting additional challenges regarding the integrity and authenticity of digital images. Consequently, the identification of AI-generated images has become a critical problem in image forensics. However, there is a lack of literature addressing the detection of images generated by diffusion models. In this article, our focus is on developing a model capable of detecting images generated through both GAN techniques and diffusion models. We propose DiffCoR, a novel detection method for identifying AI-generated images. It consists of two main modules: Stable Diffusion Processing (SDP) and Image Representation Learning (IRL). The SDP module uses a pre-trained Stable Diffusion model to reconstruct input images via reverse diffusion and captures subtle manipulations through reconstruction discrepancies. The IRL module applies self-supervised learning with Latent Consistency Loss (LCL) to extract robust, invariant features, ensuring consistent latent representations across augmented views. We also incorporate frequency domain analysis using Discrete Fourier Transform (DFT) to enhance manipulation detection. Additionally, we introduce ForensicsImage, a publicly available dataset of over 400,000 real and AI-generated images from LSUN-Bedroom, CelebA-HQ, CelebDFv2, and various diffusion models. Experiments on ForensicsImage and GenImage show that DiffCoR achieves state-of-the-art performance, with strong cross-dataset generalization, making it suitable for real-world use in digital forensics, content verification, and social media moderation.