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researcher

Jingyi Wang

23 papers hereh-index 161.1k citations42 works total

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

author position
  • first author6
  • middle author13
  • last author2

Across the 21 of 23 papers where every author was matched, so the position is known.

fields
  • cs.LG7
  • cs.SE7
  • cs.CR4
  • cs.AI2
  • cond-mat.stat-mech1
  • cs.CL1
same name
  • Jingyi Wang — 14 papers, h 7
  • Jingyi Wang — 7 papers, h 19
  • Jingyi Wang — 7 papers, h 3
  • Jingyi Wang — 7 papers, h 2
  • Jingyi Wang — 6 papers, h 2
  • Jingyi Wang — 6 papers, h 4

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
20162025
most citedAdversarial Sample Detection for Deep Neural Network through Model Mutation Testing

153 citations · 322 across the 21 of their papers we have counts for

collaborators
Showing cs.CRShow all

4 papers · 1 filter

cs.CR2021

Better Pay Attention Whilst Fuzzing

Shunkai Zhu, Jingyi Wang, Jun Sun +4

Fuzzing is one of the prevailing methods for vulnerability detection. However, even state-of-the-art fuzzing methods become ineffective after some period of time, i.e., the coverag…

cs.CR2021

Copy, Right? A Testing Framework for Copyright Protection of Deep Learning Models

Jialuo Chen, Jingyi Wang, Tinglan Peng +6

Deep learning (DL) models, especially those large-scale and high-performance ones, can be very costly to train, demanding a great amount of data and computational resources. Unauth…

cs.CR2021★ 48 cited

Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems

Yifan Jia, Jingyi Wang, Christopher M. Poskitt +3

The threats faced by cyber-physical systems (CPSs) in critical infrastructure have motivated research into a multitude of attack detection mechanisms, including anomaly detectors b…

cs.CR2021★ 2 cited

Attack as Defense: Characterizing Adversarial Examples using Robustness

Zhe Zhao, Guangke Chen, Jingyi Wang +3

As a new programming paradigm, deep learning has expanded its application to many real-world problems. At the same time, deep learning based software are found to be vulnerable to…

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