  Mammalian brains handle complex reasoning by integrating information across
brain regions specialized for particular sensory modalities. This enables
improved robustness and generalization versus deep neural networks, which
typically process one modality and are vulnerable to perturbations. While
defense methods exist, they do not generalize well across perturbations. We
developed a fusion model combining background and foreground features from CNNs
trained on Imagenet and Places365. We tested its robustness to
human-perceivable perturbations on MS COCO. The fusion model improved
robustness, especially for classes with greater context variability. Our
proposed solution for integrating multiple modalities provides a new approach
to enhance robustness and may be complementary to existing methods.
