# Attesting Model Lineage by Consisted Knowledge Evolution with Fine-Tuning Trajectory

arXiv: https://arxiv.org/abs/2601.11683 · HTML: https://mlsanity.com/paper/2601.11683/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 52 references that are in the map.
- First posted on arXiv 16 Jan 2026.

## Builds on (most cited first, 30 of 37)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [Fine-Grained Visual Classification of Aircraft](https://mlsanity.com/paper/1306.5151/) (2013)
- [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023)
- [Editing Models with Task Arithmetic](https://mlsanity.com/paper/2212.04089/) (2022)
- [Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion](https://mlsanity.com/paper/1912.08795/) (2019)
- [Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring](https://mlsanity.com/paper/1802.04633/) (2018)
- [Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models](https://mlsanity.com/paper/2305.12827/) (2023)
- [FreeU: Free Lunch in Diffusion U-Net](https://mlsanity.com/paper/2309.11497/) (2023)
- [Resnet in Resnet: Generalizing Residual Architectures](https://mlsanity.com/paper/1603.08029/) (2016)
- [IPGuard: Protecting Intellectual Property of Deep Neural Networks via Fingerprinting the Classification Boundary](https://mlsanity.com/paper/1910.12903/) (2019)
- [Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations](https://mlsanity.com/paper/2202.08602/) (2022)
- [LLMmap: Fingerprinting For Large Language Models](https://mlsanity.com/paper/2407.15847/) (2024)
- [Deep Model Intellectual Property Protection via Deep Watermarking](https://mlsanity.com/paper/2103.04980/) (2021)
- [Model Zoo: A Growing "Brain" That Learns Continually](https://mlsanity.com/paper/2106.03027/) (2021)
- [Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100](https://mlsanity.com/paper/2108.04062/) (2021)
- [Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity Attack](https://mlsanity.com/paper/2102.04362/) (2021)
- [SSLGuard: A Watermarking Scheme for Self-supervised Learning Pre-trained Encoders](https://mlsanity.com/paper/2201.11692/) (2022)
- [Origin Tracing and Detecting of LLMs](https://mlsanity.com/paper/2304.14072/) (2023)
- [Model Zoos: A Dataset of Diverse Populations of Neural Network Models](https://mlsanity.com/paper/2209.14764/) (2022)
- [When is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers](https://mlsanity.com/paper/2504.10957/) (2025)
- [Model Editing by Standard Fine-Tuning](https://mlsanity.com/paper/2402.11078/) (2024)
- [FT-Shield: A Watermark Against Unauthorized Fine-tuning in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2310.02401/) (2023)
- [False Claims against Model Ownership Resolution](https://mlsanity.com/paper/2304.06607/) (2023)
- [Knowledge Consistency between Neural Networks and Beyond](https://mlsanity.com/paper/1908.01581/) (2019)
- [Identifying Appropriate Intellectual Property Protection Mechanisms for Machine Learning Models: A Systematization of Watermarking, Fingerprinting, Model Access, and Attacks](https://mlsanity.com/paper/2304.11285/) (2023)

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

- [DRGW: Learning Disentangled Representations for Robust Graph Watermarking](https://mlsanity.com/paper/2601.13569/) (2026)

## Abstract (arXiv)

The fine-tuning technique in deep learning gives rise to an emerging lineage relationship among models. This lineage provides a promising perspective for addressing security concerns such as unauthorized model redistribution and false claim of model provenance, which are particularly pressing in textcolorblueopen-weight model libraries where robust lineage verification mechanisms are often lacking. Existing approaches to model lineage detection primarily rely on static architectural similarities, which are insufficient to capture the dynamic evolution of knowledge that underlies true lineage relationships. Drawing inspiration from the genetic mechanism of human evolution, we tackle the problem of model lineage attestation by verifying the joint trajectory of knowledge evolution and parameter modification. To this end, we propose a novel model lineage attestation framework. In our framework, model editing is first leveraged to quantify parameter-level changes introduced by fine-tuning. Subsequently, we introduce a novel knowledge vectorization mechanism that refines the evolved knowledge within the edited models into compact representations by the assistance of probe samples. The probing strategies are adapted to different types of model families. These embeddings serve as the foundation for verifying the arithmetic consistency of knowledge relationships across models, thereby enabling robust attestation of model lineage. Extensive experimental evaluations demonstrate the effectiveness and resilience of our approach in a variety of adversarial scenarios in the real world. Our method consistently achieves reliable lineage verification across a broad spectrum of model types, including classifiers, diffusion models, and large language models.