  Pruning is a well-known mechanism for reducing the computational cost of deep
convolutional networks. However, studies have shown the potential of pruning as
a form of regularization, which reduces overfitting and improves
generalization. We demonstrate that this family of strategies provides
additional benefits beyond computational performance and generalization. Our
analyses reveal that pruning structures (filters and/or layers) from
convolutional networks increase not only generalization but also robustness to
adversarial images (natural images with content modified). Such achievements
are possible since pruning reduces network capacity and provides
regularization, which have been proven effective tools against adversarial
images. In contrast to promising defense mechanisms that require training with
adversarial images and careful regularization, we show that pruning obtains
competitive results considering only natural images (e.g., the standard and
low-cost training). We confirm these findings on several adversarial attacks
and architectures; thus suggesting the potential of pruning as a novel defense
mechanism against adversarial images.
