Stateful-CCSH: An Efficient Authentication Scheme for High-Resolution Video Surveillance System

Li Tang, Qingqing Ye, Huadi Zheng, Haibo Hu, Ziyang Han, Ngai Fong Law

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Video cameras have been widely deployed for surveillance and smart city. But the authenticity of a video scene faces a great challenge due to the ease of tampering with video data without leaving visible traces. Unfortunately, existing authentication schemes are not efficient for emerging high-resolution videos. In this paper, we propose Stateful-CCSH which adopts correlation coefficient sampling in image hash and learns from previous frames to sample those blocks with more dynamic contents and thus more likely to be tampered in a video forgery. To decrease the impact of false detection introduced by compression and sampling, we also propose a group smoothing based authentication scheme. The experimental results show that Stateful-CCSH not only achieves excellent performance in forgery detection, particularly the detection of moving object removal, but also saves significant computation and communication costs even when the video resolutions are high.

Original languageEnglish
Article numberArticle number 9751595
JournalIEEE Internet of Things Journal
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Authentication
  • Correlation
  • correlation coefficient sampling
  • Feature extraction
  • Forgery
  • object removal detection
  • Random sampling
  • Streaming media
  • Surveillance
  • video authentication
  • video surveillance system.
  • Watermarking

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

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