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Which Model Generated This Image? A Model-Agnostic Approach for Origin Attribution

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Computer Vision – ECCV 2024 (ECCV 2024)

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Abstract

Recent progress in visual generative models enables the generation of high-quality images. To prevent the misuse of generated images, it is important to identify the origin model that generates them. In this work, we study the origin attribution of generated images in a practical setting where only a few images generated by a source model are available and the source model cannot be accessed. The goal is to check if a given image is generated by the source model. We first formulate this problem as a few-shot one-class classification task. To solve the task, we propose OCC-CLIP, a CLIP-based framework for few-shot one-class classification, enabling the identification of an image’s source model, even among multiple candidates. Extensive experiments corresponding to various generative models verify the effectiveness of our OCC-CLIP framework. Furthermore, an experiment based on the recently released DALL\(\cdot \)E-3 API verifies the real-world applicability of our solution. Our source code is available at https://github.com/uwFengyuan/OCC-CLIP.

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Acknowledgement

This work is supported by the UKRI grant: Turing AI Fellowship EP/W002981/1, EPSRC/MURI grant: EP/N019474/1. We thank the Royal Academy of Engineering.

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Liu, F., Luo, H., Li, Y., Torr, P., Gu, J. (2025). Which Model Generated This Image? A Model-Agnostic Approach for Origin Attribution. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15120. Springer, Cham. https://doi.org/10.1007/978-3-031-73033-7_16

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