Detection of Image Seam Carving Using a Novel Pattern
Joint Authors
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-11-11
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Lu, Ming& Niu, Shaozhang. 2019. Detection of Image Seam Carving Using a Novel Pattern. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-15.
https://search.emarefa.net/detail/BIM-1129695
Modern Language Association (MLA)
Lu, Ming& Niu, Shaozhang. Detection of Image Seam Carving Using a Novel Pattern. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-15.
https://search.emarefa.net/detail/BIM-1129695
American Medical Association (AMA)
Lu, Ming& Niu, Shaozhang. Detection of Image Seam Carving Using a Novel Pattern. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-15.
https://search.emarefa.net/detail/BIM-1129695
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1129695