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Hengwei Zhang

4 papers hereh-index 393 citations28 works total

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

author position
  • middle author3
  • last author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV4
same name
  • Hengwei Zhang — 2 papers, h 1
  • Hengwei Zhang — 2 papers, h 1
  • Hengwei Zhang — 1 paper, h 7
  • Hengwei Zhang — 1 paper, h 6
  • Hengwei Zhang — 1 paper, h 9

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
20212025
most citedRandom Transformation of Image Brightness for Adversarial Attack

3 citations · 4 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CV2025

Use as Many Surrogates as You Want: Selective Ensemble Attack to Unleash Transferability without Sacrificing Resource Efficiency

Bo Yang, Hengwei Zhang, Jindong Wang +4

In surrogate ensemble attacks, using more surrogate models yields higher transferability but lower resource efficiency. This practical trade-off between transferability and efficie…

cs.CV2024★ 1 cited

Adversarial Example Soups: Improving Transferability and Stealthiness for Free

Bo Yang, Hengwei Zhang, Jindong Wang +4

Transferable adversarial examples cause practical security risks since they can mislead a target model without knowing its internal knowledge. A conventional recipe for maximizing…

cs.CV2021

Adversarial example generation with AdaBelief Optimizer and Crop Invariance

Bo Yang, Hengwei Zhang, Yuchen Zhang +2

Deep neural networks are vulnerable to adversarial examples, which are crafted by applying small, human-imperceptible perturbations on the original images, so as to mislead deep ne…

cs.CV2021★ 3 cited

Random Transformation of Image Brightness for Adversarial Attack

Bo Yang, Kaiyong Xu, Hengjun Wang +1

Deep neural networks are vulnerable to adversarial examples, which are crafted by adding small, human-imperceptible perturbations to the original images, but make the model output…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.