An Abstraction-Refinement Approach to Verifying Convolutional Neural Networks

Understand

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.