Model Lineage Analysis: Determination and Closeness Measurement

As model modification techniques are increasingly employed to obtain high-performing machine learning models at reduced costs, identifying lineage relationships-i.e., whether one model is derived from another-has garnered significant research interest. However, existing approaches are largely empirical, lack theoretical grounding, and are often ineffective against high-impact modifications. Furthermore, none have addressed the measurement of lineage closeness, which quantifies the degree of modification between models. In this paper, we reformulate the model lineage determination problem as a question of whether two models' parameters reside within the same local optimum of the loss landscape. Based on this formulation, we propose a simple yet effective method for lineage determination. We further explore the impact of various modification techniques on models' decision boundaries using visualization techniques, and observe that changes in decision boundaries serve as an accurate metric for lineage closeness. Leveraging this insight, we propose a task-agnostic and modification-type-agnostic method to measure lineage closeness by computing the mean adversarial distance from data points to decision boundaries and the matching rate of data point predictions. To reduce computational overhead, we design an efficient sampling strategy for data point selection. Extensive experiments demonstrate that our approach achieves 100% accuracy in model lineage determination and provides precise, quantitative measurements of lineage closeness across a wide range of modification scenarios.