  Universal Adversarial Perturbations (UAPs) are imperceptible, image-agnostic
vectors that cause deep neural networks (DNNs) to misclassify inputs with high
probability. In practical attack scenarios, adversarial perturbations may
undergo transformations such as changes in pixel intensity, scaling, etc.
before being added to DNN inputs. Existing methods do not create UAPs robust to
these real-world transformations, thereby limiting their applicability in
practical attack scenarios. In this work, we introduce and formulate UAPs
robust against real-world transformations. We build an iterative algorithm
using probabilistic robustness bounds and construct such UAPs robust to
transformations generated by composing arbitrary sub-differentiable
transformation functions. We perform an extensive evaluation on the popular
CIFAR-10 and ILSVRC 2012 datasets measuring our UAPs' robustness under a wide
range common, real-world transformations such as rotation, contrast changes,
etc. We further show that by using a set of primitive transformations our
method can generalize well to unseen transformations such as fog, JPEG
compression, etc. Our results show that our method can generate UAPs up to 23%
more robust than state-of-the-art baselines.

Machine Learning;Cryptography and Security