Exposing the Fake: Effective Diffusion-Generated Images Detection
Ruipeng Ma Jinhao Duan Fei Kong Xiaoshuang Shi Kaidi Xu
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
$\text{SeDID}_{\text{Stat}}$ and neural network-based
$\text{SeDID}_{\text{NNs}}$, 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.