Toward adaptive BDCT feature representation based image splicing measurement in smart cities

Xiang Lin, Shi Lin Wang*, Wei Jun Huang, Alan Wee Chung Liew, Xiao Sa Huang, Jun Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


In smart cities, digital image splicing measurement is very important to ensure the security and safety of city monitoring, environment data fusion, cognitive decisions, etc. However, due to images obtained from various environments of cities usually face malevolence splicing, it is hard to perform the authenticity of a legitimate image from smart cities. In this paper, a novel block Discrete Cosine Transform (BDCT) coefficients feature distribution based statistical approach is proposed to discover image forgeries for image splicing measurement. In the proposed feature, all the BDCT neighbouring modes are categorized into a number of groups following the maximum likelihood (ML) criterion to ensure the modes in the same group having similar distributions. For each group, the transition probability matrix (TPM) or the joint probability matrix (JPM) is extracted from the BDCT coefficient pairs in the image. Moreover, the proposed scheme is constructed by concatenating all the TPM/JPM features for each group. Experimental results demonstrate that our feature outperforms two state-of-the-art approaches when taking both the measurement accuracy and feature dimension into consideration.

Original languageEnglish
Pages (from-to)61-69
Number of pages9
JournalMeasurement: Journal of the International Measurement Confederation
Publication statusPublished - 2019 Jun
Externally publishedYes


  • BDCT coefficients
  • Digital image forensics
  • Image splicing measurement
  • Joint probability matrix
  • Transition probability matrix

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering


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