  Convolutional neural networks have gained vast popularity due to their
excellent performance in the fields of computer vision, image processing, and
others. Unfortunately, it is now well known that convolutional networks often
produce erroneous results - for example, minor perturbations of the inputs of
these networks can result in severe classification errors. Numerous
verification approaches have been proposed in recent years to prove the absence
of such errors, but these are typically geared for fully connected networks and
suffer from exacerbated scalability issues when applied to convolutional
networks. To address this gap, we present here the Cnn-Abs framework, which is
particularly aimed at the verification of convolutional networks. The core of
Cnn-Abs is an abstraction-refinement technique, which simplifies the
verification problem through the removal of convolutional connections in a way
that soundly creates an over-approximation of the original problem; and which
restores these connections if the resulting problem becomes too abstract.
Cnn-Abs is designed to use existing verification engines as a backend, and our
evaluation demonstrates that it can significantly boost the performance of a
state-of-the-art DNN verification engine, reducing runtime by 15.7% on average.
