  Machine Learning is becoming a pivotal aspect of many systems today, offering
newfound performance on classification and prediction tasks, but this rapid
integration also comes with new unforeseen vulnerabilities. To harden these
systems the ever-growing field of Adversarial Machine Learning has proposed new
attack and defense mechanisms. However, a great asymmetry exists as these
defensive methods can only provide security to certain models and lack
scalability, computational efficiency, and practicality due to overly
restrictive constraints. Moreover, newly introduced attacks can easily bypass
defensive strategies by making subtle alterations. In this paper, we study an
alternate approach inspired by honeypots to detect adversaries. Our approach
yields learned models with an embedded watermark. When an adversary initiates
an interaction with our model, attacks are encouraged to add this predetermined
watermark stimulating detection of adversarial examples. We show that
HoneyModels can reveal 69.5% of adversaries attempting to attack a Neural
Network while preserving the original functionality of the model. HoneyModels
offer an alternate direction to secure Machine Learning that slightly affects
the accuracy while encouraging the creation of watermarked adversarial samples
detectable by the HoneyModel but indistinguishable from others for the
adversary.
