Oblivious Image Matching
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.
Unable to display preview. Download preview PDF.
- [AB06]S. Avidan and M. Butman. Blind vision. In European Conference on Computer Vision, pages 1–13, 2006.Google Scholar
- [AEMM07]S. Avidan, A. Elbaz, T. Malkin, and R. Moriarty. Oblivious image matching. Technical Report cucs-030-07, Columbia University, 2007.Google Scholar
- [CGKS95]B. Chor, O. Goldreich, E. Kushilevitz, and M. Sudan. Private information retrieval. In IEEE Symposium on Foundations of Computer Science, pages 41–50, 1995.Google Scholar
- [FFFP04]L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples. In Workshop on Generative-Model Based Vision, IEEE Proc. CVPR, 2004.Google Scholar
- [FNP04]M. Freedman, K. Nissim, and B. Pinkas. Efficient private matching and set intersection. In Advances in Cryptology – (EUROCRYPT 2004), volume 3027, pages 1–19, Springer-Verlag, 2004.Google Scholar
- [Gol04]O. Goldreich. Foundations of Cryptography. Cambridge University Press, 2004.Google Scholar
- [Hof99]T. Hofmann. Probabilistic latent semantic indexing. In SIGIR, pages 50–57, 1999.Google Scholar
- [NS06]D. Nistér and H. Stewénius. Scalable recognition with a vocabulary tree. In IEEE Conference on Computer Vision and Pattern Recognition, pages 2161–2168, 2006.Google Scholar
- [SZ03]J. Sivic and A. Zisserman. Video google: A text retrieval approach to object matching in videos. International Conference on Computer Vision, pages 1470–1477, 2003.Google Scholar
- [SZ08]J. Sivic and A. Zisserman. Efficient visual search for objects in videos. Proceedings of the IEEE, 96(4), 2008.Google Scholar
- [Yao82]A. C. Yao. Protocols for secure computations. In Proc. 23rd IEEE Symposium on Foundations of Comp. Science, pages 160–164, Chicago, 1982.Google Scholar