Detection of Image Seam Carving Using a Novel Pattern
Seam carving is an excellent content-aware image resizing technology widely used, and it is also a means of image tampering. Once an image is seam carved, the distribution of magnitude levels for the pixel intensity differences in the local neighborhood will be changed, which can be considered as a...
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2019-01-01
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Series: | Computational Intelligence and Neuroscience |
Online Access: | http://dx.doi.org/10.1155/2019/9492358 |
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doaj-f8790e2192eb464ab111b9abc20c78bc2020-11-25T02:10:31ZengHindawi LimitedComputational Intelligence and Neuroscience1687-52651687-52732019-01-01201910.1155/2019/94923589492358Detection of Image Seam Carving Using a Novel PatternMing Lu0Shaozhang Niu1Beijing Key Lab of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaBeijing Key Lab of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSeam carving is an excellent content-aware image resizing technology widely used, and it is also a means of image tampering. Once an image is seam carved, the distribution of magnitude levels for the pixel intensity differences in the local neighborhood will be changed, which can be considered as a clue for detection of seam carving for forensic purposes. In order to accurately describe the distribution of magnitude levels for the pixel intensity differences in the local neighborhood, local neighborhood magnitude occurrence pattern (LNMOP) is proposed in this paper. The LNMOP pattern describes the distribution of intensity difference by counting up the number of magnitude level occurrences in the local neighborhood. Based on this, a forensic approach for image seam carving is proposed in this paper. Firstly, the histogram features of LNMOP and HOG (histogram of oriented gradient) are extracted from the images for seam carving forgery detection. Then, the final features for the classifier are selected from the extracted LNMOP features. The LNMOP feature selection method based on HOG feature hierarchical matching is proposed, which determines the LNMOP features to be selected by the HOG feature level. Finally, support vector machine (SVM) is utilized as a classifier to train and test by the above selected features to distinguish tampered images from normal images. In order to create training sets and test sets, images are extracted from the UCID image database. The experimental results of a large number of test images show that the proposed approach can achieve an overall better performance than the state-of-the-art approaches.http://dx.doi.org/10.1155/2019/9492358 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ming Lu Shaozhang Niu |
spellingShingle |
Ming Lu Shaozhang Niu Detection of Image Seam Carving Using a Novel Pattern Computational Intelligence and Neuroscience |
author_facet |
Ming Lu Shaozhang Niu |
author_sort |
Ming Lu |
title |
Detection of Image Seam Carving Using a Novel Pattern |
title_short |
Detection of Image Seam Carving Using a Novel Pattern |
title_full |
Detection of Image Seam Carving Using a Novel Pattern |
title_fullStr |
Detection of Image Seam Carving Using a Novel Pattern |
title_full_unstemmed |
Detection of Image Seam Carving Using a Novel Pattern |
title_sort |
detection of image seam carving using a novel pattern |
publisher |
Hindawi Limited |
series |
Computational Intelligence and Neuroscience |
issn |
1687-5265 1687-5273 |
publishDate |
2019-01-01 |
description |
Seam carving is an excellent content-aware image resizing technology widely used, and it is also a means of image tampering. Once an image is seam carved, the distribution of magnitude levels for the pixel intensity differences in the local neighborhood will be changed, which can be considered as a clue for detection of seam carving for forensic purposes. In order to accurately describe the distribution of magnitude levels for the pixel intensity differences in the local neighborhood, local neighborhood magnitude occurrence pattern (LNMOP) is proposed in this paper. The LNMOP pattern describes the distribution of intensity difference by counting up the number of magnitude level occurrences in the local neighborhood. Based on this, a forensic approach for image seam carving is proposed in this paper. Firstly, the histogram features of LNMOP and HOG (histogram of oriented gradient) are extracted from the images for seam carving forgery detection. Then, the final features for the classifier are selected from the extracted LNMOP features. The LNMOP feature selection method based on HOG feature hierarchical matching is proposed, which determines the LNMOP features to be selected by the HOG feature level. Finally, support vector machine (SVM) is utilized as a classifier to train and test by the above selected features to distinguish tampered images from normal images. In order to create training sets and test sets, images are extracted from the UCID image database. The experimental results of a large number of test images show that the proposed approach can achieve an overall better performance than the state-of-the-art approaches. |
url |
http://dx.doi.org/10.1155/2019/9492358 |
work_keys_str_mv |
AT minglu detectionofimageseamcarvingusinganovelpattern AT shaozhangniu detectionofimageseamcarvingusinganovelpattern |
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