Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
11002895 | Signal Processing: Image Communication | 2018 | 9 Pages |
Abstract
Image sharpening is a basic digital image processing scheme utilized to pursue better image visual quality. From image forensics point of view, revealing the processing history is essential to the content authentication of a given image. Hence, image sharpening detection has attracted increasing attention from researchers. In this paper, a convolutional neural network (CNN) based architecture is reported to detect unsharp masking (USM), the most commonly used sharpening algorithm, applied to digital images. Extensive experiments have been conducted on two benchmark image datasets. The reported results have shown the superiority of the proposed CNN based method over the existed sharpening detection method, i.e., edge perpendicular ternary coding (EPTC).
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Jingyu Ye, Zhangyi Shen, Piyush Behrani, Feng Ding, Yun-Qing Shi,