Expose Spliced Photographic Basing on Boundary and Noise Features
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E98-D No.7 pp.1426-1429
- Publication Date
- 2015/07/01
- Publicized
- 2015/04/01
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2014EDL8232
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Keyword
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Jun HOU, Yan CHENG, "Expose Spliced Photographic Basing on Boundary and Noise Features" in IEICE TRANSACTIONS on Information,
vol. E98-D, no. 7, pp. 1426-1429, July 2015, doi: 10.1587/transinf.2014EDL8232.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDL8232/_p
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@ARTICLE{e98-d_7_1426,
author={Jun HOU, Yan CHENG, },
journal={IEICE TRANSACTIONS on Information},
title={Expose Spliced Photographic Basing on Boundary and Noise Features},
year={2015},
volume={E98-D},
number={7},
pages={1426-1429},
abstract={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.},
keywords={},
doi={10.1587/transinf.2014EDL8232},
ISSN={1745-1361},
month={July},}
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TY - JOUR
TI - Expose Spliced Photographic Basing on Boundary and Noise Features
T2 - IEICE TRANSACTIONS on Information
SP - 1426
EP - 1429
AU - Jun HOU
AU - Yan CHENG
PY - 2015
DO - 10.1587/transinf.2014EDL8232
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2015
AB - 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.
ER -
English
