With recent text-to-image models, anyone can generate deceptively realistic
images with arbitrary contents, fueling the growing threat of visual
disinformation. A key enabler for generating high-resolution images with low
computational cost has been the development of latent diffusion models (LDMs).
In contrast to conventional diffusion models, LDMs perform the denoising
process in the low-dimensional latent space of a pre-trained autoencoder (AE)
instead of the high-dimensional image space. Despite their relevance, the
forensic analysis of LDMs is still in its infancy. In this work we propose
AEROBLADE, a novel detection method which exploits an inherent component of
LDMs: the AE used to transform images between image and latent space. We find
that generated images can be more accurately reconstructed by the AE than real
images, allowing for a simple detection approach based on the reconstruction
error. Most importantly, our method is easy to implement and does not require
any training, yet nearly matches the performance of detectors that rely on
extensive training. We empirically demonstrate that AEROBLADE is effective
against state-of-the-art LDMs including Stable Diffusion and Midjourney. Beyond
detection, our approach allows for the qualitative analysis of images, which
can be leveraged for identifying inpainted regions.

利用图像和潜在空间之间的重建误差，AEROBLADE 方法可以有效检测使用低维扩散模型生成的图像并允许图像的定性分析。