  Adversarial attacks can easily fool object recognition systems based on deep
neural networks (DNNs). Although many defense methods have been proposed in
recent years, most of them can still be adaptively evaded. One reason for the
weak adversarial robustness may be that DNNs are only supervised by category
labels and do not have part-based inductive bias like the recognition process
of humans. Inspired by a well-known theory in cognitive psychology --
recognition-by-components, we propose a novel object recognition model ROCK
(Recognizing Object by Components with human prior Knowledge). It first
segments parts of objects from images, then scores part segmentation results
with predefined human prior knowledge, and finally outputs prediction based on
the scores. The first stage of ROCK corresponds to the process of decomposing
objects into parts in human vision. The second stage corresponds to the
decision process of the human brain. ROCK shows better robustness than
classical recognition models across various attack settings. These results
encourage researchers to rethink the rationality of currently widely-used
DNN-based object recognition models and explore the potential of part-based
models, once important but recently ignored, for improving robustness.
