Data-Free Model Extraction Attacks in the Context of Object Detection
Harshit Shah Aravindhan G Pavan Kulkarni Yuvaraj Govidarajulu Manojkumar Parmar
A significant number of machine learning models are vulnerable to model
extraction attacks, which focus on stealing the models by using specially
curated queries against the target model. This task is well accomplished by
using part of the training data or a surrogate dataset to train a new model
that mimics a target model in a white-box environment. In pragmatic situations,
however, the target models are trained on private datasets that are
inaccessible to the adversary. The data-free model extraction technique
replaces this problem when it comes to using queries artificially curated by a
generator similar to that used in Generative Adversarial Nets. We propose for
the first time, to the best of our knowledge, an adversary black box attack
extending to a regression problem for predicting bounding box coordinates in
object detection. As part of our study, we found that defining a loss function
and using a novel generator setup is one of the key aspects in extracting the
target model. We find that the proposed model extraction method achieves
significant results by using reasonable queries. The discovery of this object
detection vulnerability will support future prospects for securing such models.