Are Images Indistinguishable to Humans Also Indistinguishable to Classifiers?

Zebin You, Xinyu Zhang, Hanzhong Allan Guo, Jingdong Wang, Chongxuan Li

Keywords: synthesis data, diffusion models, generated data, image classification, distribution classification task

TL;DR: Our paper proposes the distribution classification tasks to explore a fundamental question: How far is the generated distribution from the real distribution?

Abstract: The ultimate goal of generative models is to perfectly capture the data distribution. For image generation, common metrics of visual quality (e.g., FID) and the perceived truthfulness of generated images seem to suggest that we are nearing this goal. However, through \emph{distribution classification} tasks, we reveal that, from the perspective of neural network-based classifiers, even advanced diffusion models are still far from this goal. Specifically, classifiers are able to consistently and effortlessly distinguish real images from generated ones across various settings. Moreover, we uncover an intriguing discrepancy: classifiers can easily differentiate between diffusion models with comparable performance (e.g., U-ViT-H vs. DiT-XL), but struggle to distinguish between models within the same family but of different scales (e.g., EDM2-XS vs. EDM2-XXL). Our methodology carries several important implications. First, it naturally serves as a diagnostic tool for diffusion models by analyzing specific features of generated data. Second, it sheds light on the model autophagy disorder and offers insights into the use of generated data: augmenting real data with generated data is more effective than replacing it.

Primary Area: generative models

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Submission Number: 2925