  Frequency spectrum has played a significant role in learning unique and
discriminating features for object recognition. Both low and high frequency
information present in images have been extracted and learnt by a host of
representation learning techniques, including deep learning. Inspired by this
observation, we introduce a novel class of adversarial attacks, namely
`WaveTransform', that creates adversarial noise corresponding to low-frequency
and high-frequency subbands, separately (or in combination). The frequency
subbands are analyzed using wavelet decomposition; the subbands are corrupted
and then used to construct an adversarial example. Experiments are performed
using multiple databases and CNN models to establish the effectiveness of the
proposed WaveTransform attack and analyze the importance of a particular
frequency component. The robustness of the proposed attack is also evaluated
through its transferability and resiliency against a recent adversarial defense
algorithm. Experiments show that the proposed attack is effective against the
defense algorithm and is also transferable across CNNs.
