  Classification and identification of different phases and the transitions
between them is a central task in condensed matter physics. Machine learning,
which has achieved dramatic success in a wide range of applications, holds the
promise to bring unprecedented perspectives for this challenging task. However,
despite the exciting progress made along this direction, the reliability of
machine-learning approaches likewise demands further investigation. Here, with
the nitrogen-vacancy center platform, we report the first proof-of-principle
experimental demonstration of adversarial examples in learning topological
phases. We show that, after adding a tiny amount of carefully-designed
perturbations, the experimentally observed adversarial examples can
successfully deceive a splendid phase classifier, whose prediction accuracy is
larger than $99.2\%$ on legitimate samples, with a notably high confidence. Our
results explicitly showcase the crucial vulnerability aspect of applying
machine learning techniques in classifying phases of matter, which provides an
indispensable guide for future studies in this interdisciplinary field.
