  As the deployment of deep learning models continues to expand across
industries, the threat of malicious incursions aimed at gaining access to these
deployed models is on the rise. Should an attacker gain access to a deployed
model, whether through server breaches, insider attacks, or model inversion
techniques, they can then construct white-box adversarial attacks to manipulate
the model's classification outcomes, thereby posing significant risks to
organizations that rely on these models for critical tasks. Model owners need
mechanisms to protect themselves against such losses without the necessity of
acquiring fresh training data - a process that typically demands substantial
investments in time and capital.
  In this paper, we explore the feasibility of generating multiple versions of
a model that possess different attack properties, without acquiring new
training data or changing model architecture. The model owner can deploy one
version at a time and replace a leaked version immediately with a new version.
The newly deployed model version can resist adversarial attacks generated
leveraging white-box access to one or all previously leaked versions. We show
theoretically that this can be accomplished by incorporating parameterized
hidden distributions into the model training data, forcing the model to learn
task-irrelevant features uniquely defined by the chosen data. Additionally,
optimal choices of hidden distributions can produce a sequence of model
versions capable of resisting compound transferability attacks over time.
Leveraging our analytical insights, we design and implement a practical model
versioning method for DNN classifiers, which leads to significant robustness
improvements over existing methods. We believe our work presents a promising
direction for safeguarding DNN services beyond their initial deployment.
