Model Provenance via Model DNA
Xin Mu Yu Wang Yehong Zhang Jiaqi Zhang Hui Wang Yang Xiang Yue Yu
Understanding the life cycle of the machine learning (ML) model is an
intriguing area of research (e.g., understanding where the model comes from,
how it is trained, and how it is used). This paper focuses on a novel problem
within this field, namely Model Provenance (MP), which concerns the
relationship between a target model and its pre-training model and aims to
determine whether a source model serves as the provenance for a target model.
This is an important problem that has significant implications for ensuring the
security and intellectual property of machine learning models but has not
received much attention in the literature. To fill in this gap, we introduce a
novel concept of Model DNA which represents the unique characteristics of a
machine learning model. We utilize a data-driven and model-driven
representation learning method to encode the model's training data and
input-output information as a compact and comprehensive representation (i.e.,
DNA) of the model. Using this model DNA, we develop an efficient framework for
model provenance identification, which enables us to identify whether a source
model is a pre-training model of a target model. We conduct evaluations on both
computer vision and natural language processing tasks using various models,
datasets, and scenarios to demonstrate the effectiveness of our approach in
accurately identifying model provenance.