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cs.CV2022
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…
cs.CV2021★ 1 cited
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…
cs.CV2020★ 1 cited
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…