Clustering image noise patterns by embedding and visualization for common source camera detection

Sonja Georgievska*, Rena Bakhshi, Anand Gavai, Alessio Sclocco, Ben van Werkhoven

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

12 Citations (Scopus)

Abstract

We consider the problem of clustering a large set of images based on similarities of their noise patterns. Such clustering is necessary in forensic cases in which detection of common source of images is required, when the cameras are not physically available. We propose a novel method for clustering combining low dimensional embedding, visualization, and classical clustering of the dataset based on the similarity scores. We evaluate our method on the Dresden images database showing that the methodology is highly effective.

Original languageEnglish
Pages (from-to)22-30
Number of pages9
JournalDigital Investigation
Volume23
DOIs
Publication statusPublished - Dec 2017
Externally publishedYes

Keywords

  • Clustering
  • Common image source detection
  • Digital camera identification
  • Digital forensics
  • Photo-response non-uniformity
  • n/a OA procedure

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