  A transfer function approach has recently proven effective for calibrating
deep learning (DL) algorithms in quantitative ultrasound (QUS), addressing data
shifts at both the acquisition and machine levels. Expanding on this approach,
we develop a strategy to 'steal' the functionality of a DL model from one
ultrasound machine and implement it on another, in the context of QUS. This
demonstrates the ease with which the functionality of a DL model can be
transferred between machines, highlighting the security risks associated with
deploying such models in a commercial scanner for clinical use. The proposed
method is a black-box unsupervised domain adaptation technique that integrates
the transfer function approach with an iterative schema. It does not utilize
any information related to model internals of the victim machine but it solely
relies on the availability of input-output interface. Additionally, we assume
the availability of unlabelled data from the testing machine, i.e., the
perpetrator machine. This scenario could become commonplace as companies begin
deploying their DL functionalities for clinical use. Competing companies might
acquire the victim machine and, through the input-output interface, replicate
the functionality onto their own machines. In the experiments, we used a
SonixOne and a Verasonics machine. The victim model was trained on SonixOne
data, and its functionality was then transferred to the Verasonics machine. The
proposed method successfully transferred the functionality to the Verasonics
machine, achieving a remarkable 98\% classification accuracy in a binary
decision task. This study underscores the need to establish security measures
prior to deploying DL models in clinical settings.
