Exposing the Fake: Effective Diffusion-Generated Images Detection

RuiPeng Ma, Jinhao Duan, Fei Kong, Xiaoshuang Shi, Kaidi Xu

Keywords: Diffusion Generative Models, Security and Privacy in Image Synthesis, Synthetic Image Detection

TL;DR: We introduce SeDID, a novel approach for detecting diffusion-generated images that leverages stepwise error comparison in diffusion models, enhancing detection performance and addressing associated security and privacy concerns.

Abstract:

Image synthesis has seen significant advancements with the advent of diffusion-based generative models like Denoising Diffusion Probabilistic Models (DDPM) and text-to-image diffusion models. Despite their efficacy, there is a dearth of research dedicated to detecting diffusion generated images, which could pose potential security and privacy risks. This paper addresses this gap by proposing a novel detection method called Stepwise Error for Diffusion-generated Image Detection (SeDID). Comprising statistical-based SeDID and neural network-based SeDID, SeDID exploits the unique attributes of diffusion models, namely deterministic reverse and deterministic denoising computation errors. Our evaluations demonstrate SeDID’s superior performance over existing methods when applied to diffusion
models. Thus, our work makes a pivotal contribution to distinguishing diffusion model-generated images, marking a significant step in the domain of artificial intelligence security.

Submission Number: 55