Diffusion Model Based Secure Semantic Communications with Adversarial Purification

Xintian Ren, Jun Wu*, Hansong Xu, Xiuzhen Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A new communication paradigm based on deep learning, known as semantic communication, is driving research into end-to-end data transmission in tasks such as image classification and reconstruction. However, the issue of security stemming from semantic perturbations remains largely unexplored, leading to vulnerabilities in semantic communication systems. In this paper, we propose a secure semantic communication system that utilizes the diffusion model to tackle this issue. The secure semantic communication system proposed in this paper mitigates perturbations caused by semantic-oriented attacks by employing a diffusing process at the sender side and a denoising process at the receiver side. Simulation results indicate that, compared to conventional methods, the proposed secure semantic communication system exhibits superior robustness and accuracy across various channel conditions.

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE 10th Conference on Big Data Security on Cloud, BigDataSecurity 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages130-134
Number of pages5
ISBN (Electronic)9798350389524
DOIs
Publication statusPublished - 2024
Event10th IEEE Conference on Big Data Security on Cloud, BigDataSecurity 2024 - New York City, United States
Duration: 2024 May 102024 May 12

Publication series

NameProceedings - 2024 IEEE 10th Conference on Big Data Security on Cloud, BigDataSecurity 2024

Conference

Conference10th IEEE Conference on Big Data Security on Cloud, BigDataSecurity 2024
Country/TerritoryUnited States
CityNew York City
Period24/5/1024/5/12

Keywords

  • adversarial attack
  • adversarial purification
  • diffusion model
  • Semantic communications
  • semantic-oriented attack

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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