Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Neural Networks

This paper shows that it is possible to construct practical black-box attacks with low computational cost against robust neural network architectures such as Inception v3 and Inception ResNet v2 on the ImageNet dataset, and demonstrates the fragility of these neural networks to Perlin noise, a type of procedural noise used for generating realistic textures.