Visual Feature Based Image Forgery Detection
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2018-09-25 https://doi.org/10.14419/ijet.v7i4.6.20436
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Image splicing forgery, local binary pattern, SVM, BPNN, combined k-NN. -
Abstract
In present days, the images are building up in digital form and which may hold essential information. Such images can be voluntarily forged or manipulated using the image processing tools to abuse it. It is very complicated to notice the forgery by naked eyes. In particular, the copy move forgery is enormously demanding one to expose. Hence, this paper put forwards a method to determine the copy move forgery by extracting the visual feature called speed up robust features (SURF). In the direction to quantitatively analyze the performance, the metrics namely false positive rate and true positive rate are estimated and also comparative study is carried out by previous existing methods.
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References
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How to Cite
Vaishnavi, D., Mahalakshmi, D., & Siva Rao Alapati, V. (2018). Visual Feature Based Image Forgery Detection. International Journal of Engineering & Technology, 7(4.6), 86-90. https://doi.org/10.14419/ijet.v7i4.6.20436Received date: 2018-09-29
Accepted date: 2018-09-29
Published date: 2018-09-25