Expose Spliced Photographic Basing on Boundary and Noise Features

• HOU Jun

  Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology

• CHENG Yan

  East China University of Political Science and Law

抄録

The paper proposes an algorithm to expose spliced photographs. Firstly, a graph-based segmentation, which defines a predictor to measure boundary evidence between two neighbor regions, is used to make greedy decision. Then the algorithm gets prediction error image using non-negative linear least-square prediction. For each pair of segmented neighbor regions, the proposed algorithm gathers their statistic features and calculates features of gray level co-occurrence matrix. K-means clustering is applied to create a dictionary, and the vector quantization histogram is taken as the result vector with fixed length. For a tampered image, its noise satisfies Gaussian distribution with zero mean. The proposed method checks the similarity between noise distribution and a zero-mean Gaussian distribution, and follows with the local flatness and texture measurement. Finally, all features are fed to a support vector machine classifier. The algorithm has low computational cost. Experiments show its effectiveness in exposing forgery.

収録刊行物

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E98.D (7), 1426-1429, 2015

  一般社団法人 電子情報通信学会

キーワード

• image splicing
• K-means clustering
• distribution similarity
• flatness measure
• texture feature

詳細情報

• CRID: 1390282679355353088
• NII論文ID: 130005086170
• DOI: 10.1587/transinf.2014edl8232
• ISSN: 17451361
• ISSN: 09168532
• Web Site: https://www.jstage.jst.go.jp/article/transinf/E98.D/7/E98.D_2014EDL8232/_pdf
• 本文言語コード: en
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