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


研究成果: Article査読


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.

ジャーナルMeasurement: Journal of the International Measurement Confederation
出版ステータスPublished - 2019 6

ASJC Scopus subject areas

  • 器械工学
  • 電子工学および電気工学


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