Chapter

Digital Watermarking

Volume 4283 of the series Lecture Notes in Computer Science pp 177-187

Detection of Image Splicing Based on Hilbert-Huang Transform and Moments of Characteristic Functions with Wavelet Decomposition

  • Dongdong FuAffiliated withLancaster UniversityDept. of Electrical and Computer Engineering, New Jersey Institute of Technology
  • , Yun Q. ShiAffiliated withLancaster UniversityDept. of Electrical and Computer Engineering, New Jersey Institute of Technology
  • , Wei SuAffiliated withLancaster UniversityDept. of Electrical and Computer Engineering, New Jersey Institute of Technology

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Abstract

Image splicing is a commonly used technique in image tampering. This paper presents a novel approach to passive detection of image splicing. In the proposed scheme, the image splicing detection problem is tackled as a twoclass classification problem under the pattern recognition framework. Considering the high non-linearity and non-stationarity nature of image splicing operation, a recently developed Hilbert-Huang transform (HHT) is utilized to generate features for classification. Furthermore, a well established statistical natural image model based on moments of characteristic functions with wavelet decomposition is employed to distinguish the spliced images from the authentic images. We use support vector machine (SVM) as the classifier. The initial experimental results demonstrate that the proposed scheme outperforms the prior arts.

Keywords

image splicing Hilbert-Huang transform (HHT) characteristic functions support vector machine (SVM)