Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks (CCS ’19)
Our paper on procedural noise adversarial examples has been accepted to the 26th ACM Conference on Computer and Communications Security (ACM CCS ’19). official: https://dl.acm.org/citation.cfm?id=3345660 code: https://github.com/kenny-co/procedural-advml Abstract: 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 […]