Oblivious Image Matching

  • Shai Avidan
  • Ariel Elbaz
  • Tal Malkin
  • Ryan Moriarty

Abstract

Video surveillance is an intrusive operation that violates privacy. It is therefore desirable to devise surveillance protocols that minimize or even eliminate privacy intrusion. A principled way of doing so is to resort to Secure Multi-Party methods, that are provably secure, and adapt them to various vision algorithms. In this chapter, we describe an Oblivious Image Matching protocol which is a secure protocol for image matching. Image matching is a generalization of detection and recognition tasks since detection can be viewed as matching a particular image to a given object class (i.e., does this image contain a face?) while recognition can be viewed as matching an image of a particular instance of a class to another image of the same instance (i.e., does this image contain a particular car?). And instead of applying the Oblivious Image Matching to the entire image one can apply it to various sub-images, thus solving the localization problem (i.e., where is the gun in the image?). A leading approach to object detection and recognition is the bag-offeatures approach, where each object is reduced to a set of features and matching objects is reduced to matching their corresponding sets of features. Oblivious Image Matching uses a secure fuzzy match of string and sets as its building block. In the proposed protocol, two parties, Alice and Bob, wish to match their images, without leaking additional information. We use a novel cryptographic protocol for fuzzy matching and adopt it to the bag-of-features approach. Fuzzy matching compares two sets (or strings) and declares them to match if a certain percentage of their elements match. To apply fuzzy matching to images, we represent images as a set of visual words that can be fed to the secure fuzzy matching protocol. The fusion of a novel cryptographic protocol and recent advances in computer vision results in a secure and efficient protocol for image matching. Experiments on real images are presented.

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Shai Avidan
    • 1
  • Ariel Elbaz
    • 2
  • Tal Malkin
    • 3
  • Ryan Moriarty
    • 4
  1. 1.Adobe Systems IncNewtonUSA
  2. 2.Columbia UniversityNew YorkUSA
  3. 3.Columbia UniversityNew YorkUSA
  4. 4.University of CaliforniaUSA

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