How to Trace Latent Generative Model Generated Images without Artificial Watermark?

TL;DR

This paper introduces LatentTracer, a method to identify images generated by specific latent models like Stable Diffusion without extra watermarks or modifications, by checking if images can be reconstructed from inverted latent codes.

Contribution

The paper proposes a novel latent inversion based approach that can trace generated images without requiring additional training or watermarking steps, improving practicality and efficiency.

Findings

High accuracy in distinguishing images from the inspected model

Effective on state-of-the-art latent generative models

Images are inherently watermarked by the decoder used in source models

Abstract

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.