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

2026-01-16

AI Summary

This work addresses security concerns in open-weight model repositories—such as unauthorized redistribution and falsified provenance—by proposing a model lineage authentication method that integrates knowledge evolution tracking with parameter editing analysis. The approach employs a probe-sample-based knowledge vectorization mechanism to quantify parameter modifications through model editing techniques and introduces an adaptive probing strategy to generate robust knowledge embeddings. This enables consistent lineage verification across diverse model families. By jointly modeling the trajectories of knowledge evolution and parameter modification paths for the first time, the method demonstrates strong and reliable lineage authentication capabilities across classifiers, diffusion models, and large language models, effectively resisting a range of realistic adversarial attacks.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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 \textcolor{blue}{open-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.

Problem

• model lineage
• open-weight model
• model provenance
• unauthorized redistribution
• lineage verification

Innovation

• model lineage attestation
• knowledge evolution
• fine-tuning trajectory
• knowledge vectorization
• model editing