  We propose a new adversarial attack to Deep Neural Networks for image
classification. Different from most existing attacks that directly perturb
input pixels, our attack focuses on perturbing abstract features, more
specifically, features that denote styles, including interpretable styles such
as vivid colors and sharp outlines, and uninterpretable ones. It induces model
misclassfication by injecting imperceptible style changes through an
optimization procedure. We show that our attack can generate adversarial
samples that are more natural-looking than the state-of-the-art unbounded
attacks. The experiment also supports that existing pixel-space adversarial
attack detection and defense techniques can hardly ensure robustness in the
style related feature space.
