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A Hybrid Feature Model for Seam Carving Detection

  • Jingyu Ye
  • Yun-Qing Shi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10431)

Abstract

Seam carving, as a content-aware image resizing algorithm, is widely used nowadays. In this paper, an advanced hybrid feature model is presented to reveal the trace of seam carving, especially seam carving at a low carving rate, applied to uncompressed digital images. Two groups of features are designed to capture energy variation and pixel variation caused by seam carving, respectively. As indicated by the experimental works, the state-of-the-art performance on detecting 5% and 10% carving rate cases has been improved from 81.13% and 90.26% to 85.75% and 94.87%, respectively.

Keywords

Seam carving detection Image forensics Local derivative pattern Markov transition probability Support vector machine 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringNew Jersey Institute of TechnologyNewarkUSA

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