  While the deployment of neural networks, yielding impressive results, becomes
more prevalent in various applications, their interpretability and
understanding remain a critical challenge. Network inversion, a technique that
aims to reconstruct the input space from the model's learned internal
representations, plays a pivotal role in unraveling the black-box nature of
input to output mappings in neural networks. In safety-critical scenarios,
where model outputs may influence pivotal decisions, the integrity of the
corresponding input space is paramount, necessitating the elimination of any
extraneous "garbage" to ensure the trustworthiness of the network. Binarised
Neural Networks (BNNs), characterized by binary weights and activations, offer
computational efficiency and reduced memory requirements, making them suitable
for resource-constrained environments. This paper introduces a novel approach
to invert a trained BNN by encoding it into a CNF formula that captures the
network's structure, allowing for both inference and inversion.
