  Despite recent advancements, deep neural networks are not robust against
adversarial perturbations. Many of the proposed adversarial defense approaches
use computationally expensive training mechanisms that do not scale to complex
real-world tasks such as semantic segmentation, and offer only marginal
improvements. In addition, fundamental questions on the nature of adversarial
perturbations and their relation to the network architecture are largely
understudied. In this work, we study the adversarial problem from a frequency
domain perspective. More specifically, we analyze discrete Fourier transform
(DFT) spectra of several adversarial images and report two major findings:
First, there exists a strong connection between a model architecture and the
nature of adversarial perturbations that can be observed and addressed in the
frequency domain. Second, the observed frequency patterns are largely image-
and attack-type independent, which is important for the practical impact of any
defense making use of such patterns. Motivated by these findings, we
additionally propose an adversarial defense method based on the well-known
Wiener filters that captures and suppresses adversarial frequencies in a
data-driven manner. Our proposed method not only generalizes across unseen
attacks but also beats five existing state-of-the-art methods across two models
in a variety of attack settings.
