SEAT: Similarity Encoder by Adversarial Training for Detecting Model Extraction Attack Queries

Abstract

Given black-box access to the prediction API, model extraction attacks can steal the functionality of models deployed in the cloud. In this paper, we introduce the SEAT detector, which detects black-box model extraction attacks so that the defender can terminate malicious accounts. SEAT has a similarity encoder trained by adversarial training. Using the similarity encoder, SEAT detects accounts that make queries that indicate a model extraction attack in progress and cancels these accounts. We evaluate our defense against existing model extraction attacks and against new adaptive attacks introduced in this paper. Our results show that even against adaptive attackers, SEAT increases the cost of model extraction attacks by 3.8 times to 16 times.