Detecting USM image sharpening by using CNN

Jingyu Ye, Zhangyi Shen, Piyush Behrani, Feng Ding, Yun Qing Shi

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


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).

Original languageEnglish (US)
Pages (from-to)258-264
Number of pages7
JournalSignal Processing: Image Communication
StatePublished - Oct 2018

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


  • Convolutional neural network
  • Edge perpendicular ternary coding
  • Image forensics
  • Image sharpening
  • Unsharp Masking


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