  Deep Neural Networks (DNNs) are vulnerable to adversarial examples which
would inveigle neural networks to make prediction errors with small
perturbations on the input images. Researchers have been devoted to promoting
the research on the universal adversarial perturbations (UAPs) which are
gradient-free and have little prior knowledge on data distributions. Procedural
adversarial noise attack is a data-free universal perturbation generation
method. In this paper, we propose two universal adversarial perturbation (UAP)
generation methods based on procedural noise functions: Simplex noise and
Worley noise. In our framework, the shading which disturbs visual
classification is generated with rendering technology. Without changing the
semantic representations, the adversarial examples generated via our methods
show superior performance on the attack.
