An effective method to detect seam carving

Jingyu Ye, Yun Qing Shi

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Seam carving, also known as content-aware image resizing, is the most popular image resizing algorithm nowadays. Therefore, detecting seam carving has become an important topic in image forensics. In this paper, an advanced statistical model, consisting of local derivative pattern, Markov transition probabilities, and subtractive pixel adjacency model, is proposed to determine if an image has been seam carved or not. The performance of the proposed feature set can be further improved, and the feature set's dimensionality can be largely reduced by utilizing linear support vector machine (SVM) based recursive feature elimination. With the linear SVM classifier, the experimental works have demonstrated that the proposed approach can successfully detect seam carving. It outperforms the state-of-the-art in general; in particular at the low carving rate cases, such as 5%, 10% and 20%, the average detection accuracy has been boosted from 66%, 75% and 87% to 81%, 90% and 96%, respectively. On detecting seam carving in JPEG images and geometrical transformed uncompressed images, the proposed approach has also shown promising performance.

Original languageEnglish (US)
Pages (from-to)13-22
Number of pages10
JournalJournal of Information Security and Applications
Volume35
DOIs
StatePublished - Aug 1 2017

All Science Journal Classification (ASJC) codes

  • Software
  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications

Keywords

  • Content-aware image resizing
  • Image forensics
  • Local derivative patterns
  • Markov transition probability
  • SVM based recursive feature elimination (SVM-RFE)
  • Seam carving
  • Subtractive pixel adjacency model

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