  Latent generative models (e.g., Stable Diffusion) have become more and more
popular, but concerns have arisen regarding potential misuse related to images
generated by these models. It is, therefore, necessary to analyze the origin of
images by inferring if a particular image was generated by a specific latent
generative model. Most existing methods (e.g., image watermark and model
fingerprinting) require extra steps during training or generation. These
requirements restrict their usage on the generated images without such extra
operations, and the extra required operations might compromise the quality of
the generated images. In this work, we ask whether it is possible to
effectively and efficiently trace the images generated by a specific latent
generative model without the aforementioned requirements. To study this
problem, we design a latent inversion based method called LatentTracer to trace
the generated images of the inspected model by checking if the examined images
can be well-reconstructed with an inverted latent input. We leverage gradient
based latent inversion and identify a encoder-based initialization critical to
the success of our approach. Our experiments on the state-of-the-art latent
generative models, such as Stable Diffusion, show that our method can
distinguish the images generated by the inspected model and other images with a
high accuracy and efficiency. Our findings suggest the intriguing possibility
that today's latent generative generated images are naturally watermarked by
the decoder used in the source models. Code:
https://github.com/ZhentingWang/LatentTracer.
