Variable Step-Size Adversarial Attacks Against Deep Learning Based End-to-End Autoencoder

Han Jiang; Jifa Zhang; Mingqian Liu; Nan Zhao; Arumugam Nallauathan; Yonghui Li

doi:10.1109/tvt.2025.3582045

ScienceGate Book Chapters

JOURNAL ARTICLE

Variable Step-Size Adversarial Attacks Against Deep Learning Based End-to-End Autoencoder

Han Jiang Jifa Zhang Mingqian Liu Nan Zhao Arumugam Nallauathan Yonghui Li

Year: 2025 Journal: IEEE Transactions on Vehicular Technology Vol: 74 (11)Pages: 18184-18189 Publisher: Institute of Electrical and Electronics Engineers

DOI: 10.1109/tvt.2025.3582045

Get Full-Text PDF Get Analytical Report

Keywords:

End-to-end principle Autoencoder Variable (mathematics) Artificial intelligence Computer science Deep learning Adversarial system Algorithm Mathematics

Metrics

Cited By

0.00

FWCI (Field Weighted Citation Impact)

Refs

0.09

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Topics

Adversarial Robustness in Machine Learning

Physical Sciences → Computer Science → Artificial Intelligence

Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Digital Media Forensic Detection

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Variable Step-Size Adversarial Attacks Against Deep Learning Based End-to-End Autoencoder

Metrics

Topics

Related Documents

Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems

Momentum-Based Adversarial Attacks Against End-to-End Communication Systems

Adversarial Attacks Against LipNet: End-to-End Sentence Level Lipreading

End-to-end Physical Layer Optimization Scheme Based on Deep Learning Autoencoder

A Robust Adversarial Network-Based End-to-End Communications System with Strong Generalization Ability Against Adversarial Attacks