  To guarantee that machine learning models yield outputs that are not only
accurate, but also robust, recent works propose formally verifying robustness
properties of machine learning models. To be applicable to realistic
safety-critical systems, the used verification algorithms need to manage the
combinatorial explosion resulting from vast variations in the input domain, and
be able to verify correctness properties derived from versatile and
domain-specific requirements.
  In this paper, we formalise the VoTE algorithm presented earlier as a tool
description, and extend the tool set with mechanisms for systematic scalability
studies. In particular, we show a) how the separation of property checking from
the core verification engine enables verification of versatile requirements, b)
the scalability of the tool, both in terms of time taken for verification and
use of memory, and c) that the algorithm has attractive properties that lend
themselves well for massive parallelisation.
  We demonstrate the application of the tool in two case studies, namely digit
recognition and aircraft collision avoidance, where the first case study serves
to assess the resource utilisation of the tool, and the second to assess the
ability to verify versatile correctness properties.
