1. Exposing the Fake: Effective Diffusion-Generated Images Detection
- Author
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Ma, Ruipeng, Duan, Jinhao, Kong, Fei, Shi, Xiaoshuang, and Xu, Kaidi
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Computer Science - Cryptography and Security ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,Cryptography and Security (cs.CR) ,Machine Learning (cs.LG) - 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 $\text{SeDID}_{\text{Stat}}$ 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., AdvML-Frontiers@ICML 2023
- Published
- 2023