# Decentralized Attribution of Generative Models

arXiv: https://arxiv.org/abs/2010.13974 · HTML: https://mlsanity.com/paper/2010.13974/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2020.

## Built on by (most cited first, 17 of 17)

- [R.A.C.E.: Robust Adversarial Concept Erasure for Secure Text-to-Image Diffusion Model](https://mlsanity.com/paper/2405.16341/) (2024)
- [WOUAF: Weight Modulation for User Attribution and Fingerprinting in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2306.04744/) (2023)
- [Deepfake Network Architecture Attribution](https://mlsanity.com/paper/2202.13843/) (2022)
- [Progressive Open Space Expansion for Open-Set Model Attribution](https://mlsanity.com/paper/2303.06877/) (2023)
- [ECLIPSE: A Resource-Efficient Text-to-Image Prior for Image Generations](https://mlsanity.com/paper/2312.04655/) (2023)
- [Learning to Disentangle GAN Fingerprint for Fake Image Attribution](https://mlsanity.com/paper/2106.08749/) (2021)
- [Attributing Image Generative Models using Latent Fingerprints](https://mlsanity.com/paper/2304.09752/) (2023)
- [Which Model Generated This Image? A Model-Agnostic Approach for Origin Attribution](https://mlsanity.com/paper/2404.02697/) (2024)
- [Are CLIP features all you need for Universal Synthetic Image Origin Attribution?](https://mlsanity.com/paper/2408.09153/) (2024)
- [Single-Model Attribution of Generative Models Through Final-Layer Inversion](https://mlsanity.com/paper/2306.06210/) (2023)
- [A Comprehensive Survey on Concept Erasure in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2502.14896/) (2025)
- [EraseFlow: Learning Concept Erasure Policies via GFlowNet-Driven Alignment](https://mlsanity.com/paper/2511.00804/) (2025)
- [A Survey on Responsible Generative AI: What to Generate and What Not](https://mlsanity.com/paper/2404.05783/) (2024)
- [BOSC: A Backdoor-based Framework for Open Set Synthetic Image Attribution](https://mlsanity.com/paper/2405.11491/) (2024)
- [Attributable-Watermarking of Speech Generative Models](https://mlsanity.com/paper/2202.08900/) (2022)
- [Generalized Deepfake Attribution](https://mlsanity.com/paper/2406.18278/) (2024)
- [Transferable Class-Modelling for Decentralized Source Attribution of GAN-Generated Images](https://mlsanity.com/paper/2203.09777/) (2022)

## Abstract (arXiv)

Growing applications of generative models have led to new threats such as malicious personation and digital copyright infringement. One solution to these threats is model attribution, i.e., the identification of user-end models where the contents under question are generated from. Existing studies showed empirical feasibility of attribution through a centralized classifier trained on all user-end models. However, this approach is not scalable in reality as the number of models ever grows. Neither does it provide an attributability guarantee. To this end, this paper studies decentralized attribution, which relies on binary classifiers associated with each user-end model. Each binary classifier is parameterized by a user-specific key and distinguishes its associated model distribution from the authentic data distribution. We develop sufficient conditions of the keys that guarantee an attributability lower bound. Our method is validated on MNIST, CelebA, and FFHQ datasets. We also examine the trade-off between generation quality and robustness of attribution against adversarial post-processes.