A hybrid feature model for seam carving detection

Jingyu Ye, Yun Qing Shi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationDigital Forensics and Watermarking - 16th International Workshop, IWDW 2017, Proceedings
EditorsYun-Qing Shi, Hyoung Joong Kim, Christian Kraetzer, Jana Dittmann
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783319641843
StatePublished - 2017
Event16th International Workshop on Digital Forensics and Watermarking, IWDW 2017 - Magdeburg, Germany
Duration: Aug 23 2017Aug 25 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10431 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other16th International Workshop on Digital Forensics and Watermarking, IWDW 2017

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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


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