GanTextKnockoff: stealing text sentiment analysis model functionality using synthetic data

Today, black-box machine learning models are often subject to extraction attacks that aim to retrieve their internal information. Black-box model extraction attacks are typically conducted by providing input data and, based on observing the output results, constructing a new model that functions equivalently to the original. This process is usually carried out by leveraging available data from public repositories or synthetic data generated by generative models. Most model extraction attack methods using synthetic data have been concentrated in the field of computer vision, with minimal research focused on model extraction in natural language processing. In this paper, we propose a method that utilizes synthetic textual data to construct a new model with high accuracy and similarity to the original black-box sentiment analysis model.