Analysis of seam-carving-based anonymization of images against PRNU noise pattern-based source attribution

Ahmet Emir Dirik, Husrev Taha Sencar, Nasir Memon

Research output: Contribution to journalArticlepeer-review


The availability of sophisticated source attribution techniques raises new concerns about privacy and anonymity of photographers, activists, and human right defenders who need to stay anonymous while spreading their images and videos. Recently, the use of seam-carving, a content-aware resizing method, has been proposed to anonymize the source camera of images against the well-known photoresponse nonuniformity (PRNU)-based source attribution technique. In this paper, we provide an analysis of the seam-carving-based source camera anonymization method by determining the limits of its performance introducing two adversarial models. Our analysis shows that the effectiveness of the deanonymization attacks depend on various factors that include the parameters of the seam-carving method, strength of the PRNU noise pattern of the camera, and an adversary's ability to identify uncarved image blocks in a seam-carved image. Our results show that, for the general case, there should not be many uncarved blocks larger than the size of 50 × 50 pixels for successful anonymization of the source camera.

Original languageEnglish (US)
Article number6914598
Pages (from-to)2277-2290
Number of pages14
JournalIEEE Transactions on Information Forensics and Security
Issue number12
StatePublished - Dec 1 2014


  • PRNU noise pattern
  • seam-carving
  • source attribution

ASJC Scopus subject areas

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


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