  Adversarial attacks find perturbations that can fool models into
misclassifying images. Previous works had successes in generating
noisy/edge-rich adversarial perturbations, at the cost of degradation of image
quality. Such perturbations, even when they are small in scale, are usually
easily spottable by human vision. In contrast, we propose Harmonic Adversar-
ial Attack Methods (HAAM), that generates edge-free perturbations by using
harmonic functions. The property of edge-free guarantees that the generated
adversarial images can still preserve visual quality, even when perturbations
are of large magnitudes. Experiments also show that adversaries generated by
HAAM often have higher rates of success when transferring between models. In
addition, we find harmonic perturbations can simulate natural phenomena like
natural lighting and shadows. It would then be possible to help find corner
cases for given models, as a first step to improving them.
