DREAM: Domain-Agnostic Reverse Engineering Attributes of Black-Box Model

Deep learning models are usually black boxes when deployed on machine learning platforms. Prior works have shown that the attributes (e.g., the number of convolutional layers) of a target black-box model can be exposed through a sequence of queries. There is a crucial limitation: these works assume the training dataset of the target model is known beforehand and leverage this dataset for model attribute attack. However, it is difficult to access the training dataset of the target black-box model in reality. Therefore, whether the attributes of a target black-box model could be still revealed in this case is doubtful. In this paper, we investigate a new problem of black-box reverse engineering, without requiring the availability of the target model’s training dataset. We put forward a general and principled framework DREAM, by casting this problem as out-of-distribution (OOD) generalization. In this way, we can learn a domain-agnostic meta-model to infer the attributes of the target black-box model with unknown training data. This makes our method one of the kinds that can gracefully apply to an arbitrary domain for model attribute reverse engineering with strong generalization ability. Extensive experimental results demonstrate the superiority of our proposed method over the baselines.

I. Introduction

In recent years, machine learning technology has been widely used in in many tasks such as NLP and areas such as image classification [1], [2], [3], [4], natural language processing [5], [6], [7], [8], and speech recognition [9], [10]. However, existing machine learning frameworks are complicated, which requires substantial computational resources and efforts for users to train and deploy, especially for the non-expert ones. As a result, with the benefits of usability, and cost efficiency, Machine Learning as a Service (MLaaS) has become popular. MLaaS deploys well-trained machine learning models on cloud platforms, allowing users to interact with these models via the provided APIs, making advanced ML capabilities both accessible and affordable.