  Deep Neural Networks (DNNs) are finding important applications in
safety-critical systems such as Autonomous Vehicles (AVs), where perceiving the
environment correctly and robustly is necessary for safe operation. Raising
unique challenges for assurance due to their black-box nature, DNNs pose a
fundamental problem for regulatory acceptance of these types of systems. Robust
training --- training to minimize excessive sensitivity to small changes in
input --- has emerged as one promising technique to address this challenge.
However, existing robust training tools are inconvenient to use or apply to
existing codebases and models: they typically only support a small subset of
model elements and require users to extensively rewrite the training code. In
this paper we introduce a novel framework, PaRoT, developed on the popular
TensorFlow platform, that greatly reduces the barrier to entry. Our framework
enables robust training to be performed on arbitrary DNNs without any rewrites
to the model. We demonstrate that our framework's performance is comparable to
prior art, and exemplify its ease of use on off-the-shelf, trained models and
its testing capabilities on a real-world industrial application: a traffic
light detection network.
