研究扩散模型生成图像的逼真度与伪影特征，揭示人类识别真假图像的规律。
Characterizing Photorealism and Artifacts in Diffusion Model-Generated Images
- 通过大规模实验分析图像复杂度、伪影类型等因素对识别的影响。
- 74万次观察显示，复杂场景和特定伪影显著降低人类识别准确率。
- 提出扩散模型伪影分类体系，适合媒体可信性与AI检测研究者参考。
扩散模型生成的图像看似与真实照片无异，但常包含可揭示其人工智能来源的伪影和不合理之处。鉴于此类高逼真度图像对媒体公信力构成挑战，我们开展大规模实验，评估50,444名参与者对450张扩散模型生成图像与149张真实图像的人类识别准确率，共收集749,828次观察结果和34,675条评论。结果显示，图像场景复杂度、图像内的伪影类型、图像显示时间以及人工筛选过程均显著影响人类辨别真实与生成图像的能力。此外，我们提出了一个用于描述扩散模型生成图像中常见伪影的分类体系。这些实证发现与分类框架为2024年扩散模型生成逼真图像的能力与局限提供了细致洞察。
原文摘要 · Abstract (English)
Diffusion model-generated images can appear indistinguishable from authentic photographs, but these images often contain artifacts and implausibilities that reveal their AI-generated provenance. Given the challenge to public trust in media posed by photorealistic AI-generated images, we conducted a large-scale experiment measuring human detection accuracy on 450 diffusion-model generated images and 149 real images. Based on collecting 749,828 observations and 34,675 comments from 50,444 participants, we find that scene complexity of an image, artifact types within an image, display time of an image, and human curation of AI-generated images all play significant roles in how accurately people distinguish real from AI-generated images. Additionally, we propose a taxonomy characterizing artifacts often appearing in images generated by diffusion models. Our empirical observations and taxonomy offer nuanced insights into the capabilities and limitations of diffusion models to generate photorealistic images in 2024.
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