Generalizable Origin Identification for Text-Guided Image-to-Image Diffusion Models

• URL: http://arxiv.org/abs/2501.02376v1
• Date: Sat, 04 Jan 2025 20:34:53 GMT
• Title: Generalizable Origin Identification for Text-Guided Image-to-Image Diffusion Models
• Authors: Wenhao Wang, Yifan Sun, Zongxin Yang, Zhentao Tan, Zhengdong Hu, Yi Yang,
• Abstract summary: Text-guided image-to-image diffusion models excel in translating images based on textual prompts. This motivates us to introduce the task of origin IDentification for text-guided Image-to-image Diffusion models (ID$2$) A straightforward solution to ID$2$ involves training a specialized deep embedding model to extract and compare features from both query and reference images.
• Score: 39.234894330025114
• License:
• Abstract: Text-guided image-to-image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications. However, such a powerful technique can be misused for spreading misinformation, infringing on copyrights, and evading content tracing. This motivates us to introduce the task of origin IDentification for text-guided Image-to-image Diffusion models (ID$^2$), aiming to retrieve the original image of a given translated query. A straightforward solution to ID$^2$ involves training a specialized deep embedding model to extract and compare features from both query and reference images. However, due to visual discrepancy across generations produced by different diffusion models, this similarity-based approach fails when training on images from one model and testing on those from another, limiting its effectiveness in real-world applications. To solve this challenge of the proposed ID$^2$ task, we contribute the first dataset and a theoretically guaranteed method, both emphasizing generalizability. The curated dataset, OriPID, contains abundant Origins and guided Prompts, which can be used to train and test potential IDentification models across various diffusion models. In the method section, we first prove the existence of a linear transformation that minimizes the distance between the pre-trained Variational Autoencoder (VAE) embeddings of generated samples and their origins. Subsequently, it is demonstrated that such a simple linear transformation can be generalized across different diffusion models. Experimental results show that the proposed method achieves satisfying generalization performance, significantly surpassing similarity-based methods ($+31.6\%$ mAP), even those with generalization designs.

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