Yexin Duan

Orcid: 0000-0003-0769-4773

According to our database1, Yexin Duan authored at least 18 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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In proceedings 
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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
MalPatch: Evading DNN-Based Malware Detection With Adversarial Patches.
IEEE Trans. Inf. Forensics Secur., 2024

2023
AMGmal: Adaptive mask-guided adversarial attack against malware detection with minimal perturbation.
Comput. Secur., April, 2023

Boosting Adversarial Transferability with Shallow-Feature Attack on SAR Images.
Remote. Sens., 2023

Instance attack: an explanation-based vulnerability analysis framework against DNNs for malware detection.
PeerJ Comput. Sci., 2023

2022
Understanding Universal Adversarial Attack and Defense on Graph.
Int. J. Semantic Web Inf. Syst., 2022

Enhancing transferability of adversarial examples via rotation-invariant attacks.
IET Comput. Vis., 2022

Adversarial Attack via Dual-Stage Network Erosion.
CoRR, 2022

Boosting adversarial attacks with transformed gradient.
Comput. Secur., 2022

Adversarial attack via dual-stage network erosion.
Comput. Secur., 2022

Learning Coated Adversarial Camouflages for Object Detectors.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Making Adversarial Examples More Transferable and Indistinguishable.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Meta-Knowledge Learning and Domain Adaptation for Unseen Background Subtraction.
IEEE Trans. Image Process., 2021

A data independent approach to generate adversarial patches.
Mach. Vis. Appl., 2021

A fast X-shaped foreground segmentation network with CompactASPP.
Eng. Appl. Artif. Intell., 2021

DPA: Learning Robust Physical Adversarial Camouflages for Object Detectors.
CoRR, 2021

Mask-guided noise restriction adversarial attacks for image classification.
Comput. Secur., 2021

Learning Indistinguishable and Transferable Adversarial Examples.
Proceedings of the Pattern Recognition and Computer Vision - 4th Chinese Conference, 2021

2020
Making Adversarial Examples More Transferable and Indistinguishable.
CoRR, 2020


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