PRNU-Based Camera Attribution from Multiple Seam-Carved Images

Samet Taspinar, Manoranjan Mohanty, Nasir Memon

    Research output: Contribution to journalArticle

    Abstract

    Photo response non-uniformity (PRNU) noise-based source attribution is a well-known technique to verify the camera of an image or video. Researchers have proposed various countermeasures to prevent PRNU-based source camera attribution. Forced seam-carving is one such recently proposed counter forensics technique. This technique can disable PRNU-based source camera attribution by forcefully removing seams such that the size of most uncarved image blocks is less than $50 \times 50$ pixels. In this paper, we show that given multiple seam-carved images from the same camera, source attribution can still be possible even if the size of uncarved blocks in the image is less than the recommended size of $50 \times 50$ pixels. Theoretical analysis and experiments with multiple cameras demonstrate that the effectiveness of our scheme depends on the number of seams carved from an image and the randomness of the seam positions.

    Original languageEnglish (US)
    Article number8006244
    Pages (from-to)3065-3080
    Number of pages16
    JournalIEEE Transactions on Information Forensics and Security
    Volume12
    Issue number12
    DOIs
    StatePublished - Dec 1 2017

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    Cameras
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    Experiments

    Keywords

    • countering seam-carving-based anonymization
    • PRNU noise pattern
    • source camera attribution

    ASJC Scopus subject areas

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

    Cite this

    PRNU-Based Camera Attribution from Multiple Seam-Carved Images. / Taspinar, Samet; Mohanty, Manoranjan; Memon, Nasir.

    In: IEEE Transactions on Information Forensics and Security, Vol. 12, No. 12, 8006244, 01.12.2017, p. 3065-3080.

    Research output: Contribution to journalArticle

    Taspinar, Samet ; Mohanty, Manoranjan ; Memon, Nasir. / PRNU-Based Camera Attribution from Multiple Seam-Carved Images. In: IEEE Transactions on Information Forensics and Security. 2017 ; Vol. 12, No. 12. pp. 3065-3080.
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