# Modelship Attribution: Tracing Multi-Stage Manipulations Across Generative Models

arXiv: https://arxiv.org/abs/2506.02405 · HTML: https://mlsanity.com/paper/2506.02405/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 40 references that are in the map.
- First posted on arXiv 3 Jun 2025.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Universal and Transferable Adversarial Attacks on Aligned Language Models](https://mlsanity.com/paper/2307.15043/) (2023)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [MaskGAN: Towards Diverse and Interactive Facial Image Manipulation](https://mlsanity.com/paper/1907.11922/) (2019)
- [Leveraging Frequency Analysis for Deep Fake Image Recognition](https://mlsanity.com/paper/2003.08685/) (2020)
- [3D Morphable Face Models -- Past, Present and Future](https://mlsanity.com/paper/1909.01815/) (2019)
- [Generalizing Face Forgery Detection with High-frequency Features](https://mlsanity.com/paper/2103.12376/) (2021)
- [Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data](https://mlsanity.com/paper/2007.08457/) (2020)
- [Controllable Text-to-Image Generation](https://mlsanity.com/paper/1909.07083/) (2019)
- [DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Generation Models](https://mlsanity.com/paper/2210.06998/) (2022)
- [Responsible Disclosure of Generative Models Using Scalable Fingerprinting](https://mlsanity.com/paper/2012.08726/) (2020)
- [Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing](https://mlsanity.com/paper/2104.14754/) (2021)
- [SyncTalk: The Devil is in the Synchronization for Talking Head Synthesis](https://mlsanity.com/paper/2311.17590/) (2023)
- [Domain Watermark: Effective and Harmless Dataset Copyright Protection is Closed at Hand](https://mlsanity.com/paper/2310.14942/) (2023)
- [How to Trace Latent Generative Model Generated Images without Artificial Watermark?](https://mlsanity.com/paper/2405.13360/) (2024)
- [Attributing Image Generative Models using Latent Fingerprints](https://mlsanity.com/paper/2304.09752/) (2023)
- [MMNet: Multi-Collaboration and Multi-Supervision Network for Sequential Deepfake Detection](https://mlsanity.com/paper/2307.02733/) (2023)
- [What makes you, you? Analyzing Recognition by Swapping Face Parts](https://mlsanity.com/paper/2206.11759/) (2022)
- [Generalized Deepfake Attribution](https://mlsanity.com/paper/2406.18278/) (2024)
- [PAV: Personalized Head Avatar from Unstructured Video Collection](https://mlsanity.com/paper/2407.21047/) (2024)

## Built on by (most cited first, 1 of 1)

- [Multi-Tool Image Editing Attribution in Facial Forgery](https://mlsanity.com/paper/2609.02751/) (2026)

## Abstract (arXiv)

As generative techniques become increasingly accessible, authentic visuals are frequently subjected to iterative alterations by various individuals employing a variety of tools. Currently, to avoid misinformation and ensure accountability, a lot of research on detection and attribution is emerging. Although these methods demonstrate promise in single-stage manipulation scenarios, they fall short when addressing complex real-world iterative manipulation. In this paper, we are the first, to the best of our knowledge, to systematically model this real-world challenge and introduce a novel method to solve it. We define a task called "Modelship Attribution", which aims to trace the evolution of manipulated images by identifying the generative models involved and reconstructing the sequence of edits they performed. To realistically simulate this scenario, we utilize three generative models, StyleMapGAN, DiffSwap, and FacePartsSwap, that sequentially modify distinct regions of the same image. This process leads to the creation of the first modelship dataset, comprising 83,700 images (16,740 images*5). Given that later edits often overwrite the fingerprints of earlier models, the focus shifts from extracting blended fingerprints to characterizing each model's distinctive editing patterns. To tackle this challenge, we introduce the modelship attribution transformer (MAT), a purpose-built framework designed to effectively recognize and attribute the contributions of various models within complex, multi-stage manipulation workflows. Through extensive experiments and comparative analysis with other related methods, our results, including comprehensive ablation studies, demonstrate that the proposed approach is a highly effective solution for modelship attribution.