Toward adaptive BDCT feature representation based image splicing measurement in smart cities. (June 2019)

• Record Type: Journal Article
• Authors: Lin, Xiang; Wang, Shi-Lin; Huang, Wei-Jun; Liew, Alan Wee-Chung; Huang, Xiao-Sa; Wu, Jun
• Is Part Of: Measurement. Volume 139(2019)
• Page Start: 61
• Page End: 69
• Publication Date: 2019-06
• Subjects: Image splicing measurement -- Digital image forensics -- BDCT coefficients -- Transition probability matrix -- Joint probability matrix; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2019.02.086
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Perform a comprehensive study on the discriminative power of the BDCT based features. Propose an adaptive BDCT feature representation for blind image splicing detection. Achieve better image splicing detection accuracies compared with existing methods. Abstract: 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.