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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Fu, D., Shi, Y.Q., Su, W. (2006). Detection of Image Splicing Based on Hilbert-Huang Transform and Moments of Characteristic Functions with Wavelet Decomposition. In: Shi, Y.Q., Jeon, B. (eds) Digital Watermarking. IWDW 2006. Lecture Notes in Computer Science, vol 4283. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11922841_15
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DOI: https://doi.org/10.1007/11922841_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-48825-5
Online ISBN: 978-3-540-48827-9
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