  How similar is the human mind to the sophisticated machine-learning systems
that mirror its performance? Models of object categorization based on
convolutional neural networks (CNNs) have achieved human-level benchmarks in
assigning known labels to novel images. These advances promise to support
transformative technologies such as autonomous vehicles and machine diagnosis;
beyond this, they also serve as candidate models for the visual system itself
-- not only in their output but perhaps even in their underlying mechanisms and
principles. However, unlike human vision, CNNs can be "fooled" by adversarial
examples -- carefully crafted images that appear as nonsense patterns to humans
but are recognized as familiar objects by machines, or that appear as one
object to humans and a different object to machines. This seemingly extreme
divergence between human and machine classification challenges the promise of
these new advances, both as applied image-recognition systems and also as
models of the human mind. Surprisingly, however, little work has empirically
investigated human classification of such adversarial stimuli: Does human and
machine performance fundamentally diverge? Or could humans decipher such images
and predict the machine's preferred labels? Here, we show that human and
machine classification of adversarial stimuli are robustly related: In eight
experiments on five prominent and diverse adversarial imagesets, human subjects
reliably identified the machine's chosen label over relevant foils. This
pattern persisted for images with strong antecedent identities, and even for
images described as "totally unrecognizable to human eyes". We suggest that
human intuition may be a more reliable guide to machine (mis)classification
than has typically been imagined, and we explore the consequences of this
result for minds and machines alike.
