Procedural noise is a fundamental component of computer graphics pipelines,
offering a flexible way to generate textures that exhibit "natural" random
variation. Many different types of noise exist, each produced by a separate
algorithm. In this paper, we present a single generative model which can learn
to generate multiple types of noise as well as blend between them. In addition,
it is capable of producing spatially-varying noise blends despite not having
access to such data for training. These features are enabled by training a
denoising diffusion model using a novel combination of data augmentation and
network conditioning techniques. Like procedural noise generators, the model's
behavior is controllable via interpretable parameters and a source of
randomness. We use our model to produce a variety of visually compelling noise
textures. We also present an application of our model to improving inverse
procedural material design; using our model in place of fixed-type noise nodes
in a procedural material graph results in higher-fidelity material
reconstructions without needing to know the type of noise in advance.

通过训练一种去噪扩散模型，采用数据增强和网络调节技术的新颖组合，我们提出了一个单一生成模型，可以学习生成多种类型的噪声并在它们之间进行混合，同时可以生成具有空间变化的噪声混合，而无需训练这些数据。通过控制参数和随机性，该模型的行为可由用户控制。我们还将该模型应用于改进反向程序性材料设计；使用该模型代替程序性材料图中的固定类型噪声节点可以得到更高保真度的材料重建，无需事先了解噪声类型。

统一建模空间变化噪声模式的一种噪声

One Noise to Rule Them All: Learning a Unified Model of  Spatially-Varying Noise Patterns

Deep Convolutional Networks (DCNs) have been shown to be vulnerable to
adversarial examples---perturbed inputs specifically designed to produce
intentional errors in the learning algorithms at test time. Existing
input-agnostic adversarial perturbations exhibit interesting visual patterns
that are currently unexplained. In this paper, we introduce a structured
approach for generating Universal Adversarial Perturbations (UAPs) with
procedural noise functions. Our approach unveils the systemic vulnerability of
popular DCN models like Inception v3 and YOLO v3, with single noise patterns
able to fool a model on up to 90% of the dataset. Procedural noise allows us to
generate a distribution of UAPs with high universal evasion rates using only a
few parameters. Additionally, we propose Bayesian optimization to efficiently
learn procedural noise parameters to construct inexpensive untargeted black-box
attacks. We demonstrate that it can achieve an average of less than 10 queries
per successful attack, a 100-fold improvement on existing methods. We further
motivate the use of input-agnostic defences to increase the stability of models
to adversarial perturbations. The universality of our attacks suggests that DCN
models may be sensitive to aggregations of low-level class-agnostic features.
These findings give insight on the nature of some universal adversarial
perturbations and how they could be generated in other applications.

本文介绍了一种使用程序噪声函数生成通用对抗扰动 (Universal Adversarial Perturbations，UAPs) 的方法，发现常见 DCN 模型（如 Inception v3 和 YOLO v3）具有系统漏洞，同时提出使用贝叶斯优化来构建廉价的非针对性黑盒攻击。此外，还探讨了采用输入不可知防御方法增加模型对抗扰动的稳定性，并指出 DCN 模型可能对底层类不可知特征的聚合敏感，这些结果为通用对抗扰动类型提供了启示和生成思路。