Generative AI for Physical-Layer Authentication Article Swipe

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Xiqi Cheng , Song Gao , Xiaodong Xu , Guoshun Nan , Xiaofeng Tao , Ping Zhang ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2504.18175

In recent years, Artificial Intelligence (AI)-driven Physical-Layer Authentication (PLA), which focuses on achieving endogenous security and intelligent identity authentication, has attracted considerable interest. When compared with Discriminative AI (DAI), Generative AI (GAI) offers several advantages, such as fingerprint data augmentation, fingerprint denoising and reconstruction, and protection against adversarial attacks. Inspired by these innovations, this paper provides a systematic exploration of GAI's integration into PLA frameworks. We commence with a review of representative authentication techniques, emphasizing PLA's inherent strengths. Following this, we revisit four typical GAI models and contrast the limitations of DAI with the potential of GAI in addressing PLA challenges, including insufficient fingerprint data, environment noises and inferences, perturbations in fingerprint data, and complex tasks. Specifically, we delve into providing GAI-enhanced methods for PLA across the fingerprint collection, model training, and performance optimization phases in detail. Moreover, we present a case study that combines fingerprint extrapolation and Generative Diffusion Model (GDM) to illustrate the superiority of GAI in bolstering the reliability of PLA. Additionally, we outline potential future research directions for GAI-based PLA.

Related Topics

Generative Artificial Intelligence

Generative Pre-Trained Transformer

Multi-Factor Authentication

Generative Adversarial Network

Authentication

Generative Design

Generative Grammar

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2504.18175
PDF: https://arxiv.org/pdf/2504.18175
OA Status: green
OpenAlex ID: https://openalex.org/W4416380202

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DOI: https://doi.org/10.48550/arxiv.2504.18175

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Title: Generative AI for Physical-Layer Authentication

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-04-25

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Authors: Xiqi Cheng, Song Gao, Xiaodong Xu, Guoshun Nan, Xiaofeng Tao, Ping Zhang

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Landing page: https://arxiv.org/abs/2504.18175

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PDF URL: https://arxiv.org/pdf/2504.18175

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OA status: green

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Cited by: 0

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