61 citations · 88 across the 13 of their papers we have counts for
4 papers · 1 filter
Adversarial Attack via Dual-Stage Network Erosion
Yexin Duan, Junhua Zou, Xingyu Zhou +3
Deep neural networks are vulnerable to adversarial examples, which can fool deep models by adding subtle perturbations. Although existing attacks have achieved promising results, i…
Improving the Transferability of Adversarial Examples with Resized-Diverse-Inputs, Diversity-Ensemble and Region Fitting
Junhua Zou, Zhisong Pan, Junyang Qiu +3
We introduce a three stage pipeline: resized-diverse-inputs (RDIM), diversity-ensemble (DEM) and region fitting, that work together to generate transferable adversarial examples. W…
Learning Coated Adversarial Camouflages for Object Detectors
Yexin Duan, Jialin Chen, Xingyu Zhou +5
An adversary can fool deep neural network object detectors by generating adversarial noises. Most of the existing works focus on learning local visible noises in an adversarial "pa…
Making Adversarial Examples More Transferable and Indistinguishable
Junhua Zou, Yexin Duan, Boyu Li +3
Fast gradient sign attack series are popular methods that are used to generate adversarial examples. However, most of the approaches based on fast gradient sign attack series canno…