Enabling Verification of Deep Neural Networks in Perception Tasks Using Fuzzy Logic and Concept Embeddings

Understand

One major drawback of deep convolutional neural networks (CNNs) for use in safety critical applications is their black-box nature.

• This makes it hard to verify or monitor complex, symbolic requirements on already trained computer vision CNNs.
• In this work, we present a simple, yet effective, approach to verify that a CNN complies with symbolic predicate logic rules which relate visual concepts.
• It is the first that (1) does not modify the CNN, (2) may use visual concepts that are no CNN in- or output feature, and (3) can leverage continuous CNN confidence outputs.