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Haibin Zheng

34 papers hereh-index 221.7k citations110 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author4
  • middle author19
  • last author5

Across the 28 of 34 papers where every author was matched, so the position is known.

fields
  • cs.CR10
  • cs.LG10
  • cs.CV4
  • cs.SI3
  • cs.AI2
  • cs.SE2
same name
  • Haibin Zheng — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20182024
most citedPOBA-GA: Perturbation Optimized Black-Box Adversarial Attacks via Genetic Algorithm

78 citations · 104 across the 23 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2024

LiDAttack: Robust Black-box Attack on LiDAR-based Object Detection

Jinyin Chen, Danxin Liao, Sheng Xiang +1

Since DNN is vulnerable to carefully crafted adversarial examples, adversarial attack on LiDAR sensors have been extensively studied. We introduce a robust black-box attack dubbed…

cs.CV2021

Fight Perturbations with Perturbations: Defending Adversarial Attacks via Neuron Influence

Ruoxi Chen, Haibo Jin, Haibin Zheng +2

The vulnerabilities of deep learning models towards adversarial attacks have attracted increasing attention, especially when models are deployed in security-critical domains. Numer…

cs.CV2021

Salient Feature Extractor for Adversarial Defense on Deep Neural Networks

Jinyin Chen, Ruoxi Chen, Haibin Zheng +3

Recent years have witnessed unprecedented success achieved by deep learning models in the field of computer vision. However, their vulnerability towards carefully crafted adversari…

cs.CV2018

FineFool: Fine Object Contour Attack via Attention

Jinyin Chen, Haibin Zheng, Hui Xiong +1

Machine learning models have been shown vulnerable to adversarial attacks launched by adversarial examples which are carefully crafted by attacker to defeat classifiers. Deep learn…

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