Privacy Protection in Interactive Content Based Image Retrieval

Privacy protection in Content Based Image Retrieval (CBIR) is a new research topic in cyber security and privacy. The state-of-art CBIR systems usually adopt interactive mechanism, namely relevance feedback, to enhance the retrieval precision. How to protect the user's privacy in such Relevance Feedback based CBIR (RF-CBIR) is a challenge problem. In this paper, we investigate this problem and propose a new Private Relevance Feedback CBIR (PRF-CBIR) scheme. PRF-CBIR can leverage the performance gain of relevance feedback and preserve the user's search intention at the same time. The new PRF-CBIR consists of three stages: 1) private query; 2) private feedback; 3) local retrieval. Private query performs the initial query with a privacy controllable feature vector; private feedback constructs the feedback image set by introducing confusing classes following the K-anonymity principle; local retrieval finally re-ranks the images in the user side. Privacy analysis shows that PRF-CBIR fulfills the privacy requirements. The experiments carried out on the real-world image collection confirm the effectiveness of the proposed PRF-CBIR scheme.

[1]  Thomas S. Huang,et al.  Relevance feedback: a power tool for interactive content-based image retrieval , 1998, IEEE Trans. Circuits Syst. Video Technol..

[2]  Yi Zhu,et al.  Towards Privacy-Preserving Content-Based Image Retrieval in Cloud Computing , 2018, IEEE Transactions on Cloud Computing.

[3]  B. S. Manjunath,et al.  Color and texture descriptors , 2001, IEEE Trans. Circuits Syst. Video Technol..

[4]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[5]  Shucheng Yu,et al.  SEISA: Secure and efficient encrypted image search with access control , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[6]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  G. Griffin,et al.  Caltech-256 Object Category Dataset , 2007 .

[8]  Cynthia Dwork,et al.  Differential Privacy , 2006, ICALP.

[9]  Yiannis S. Boutalis,et al.  CEDD: Color and Edge Directivity Descriptor: A Compact Descriptor for Image Indexing and Retrieval , 2008, ICVS.

[10]  Min Wu,et al.  Secure image retrieval through feature protection , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[11]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[12]  Min Wu,et al.  Confidentiality-Preserving Image Search: A Comparative Study Between Homomorphic Encryption and Distance-Preserving Randomization , 2014, IEEE Access.

[13]  Meng Wang,et al.  Enhancing Sketch-Based Image Retrieval by Re-Ranking and Relevance Feedback , 2016, IEEE Transactions on Image Processing.

[14]  Latanya Sweeney,et al.  k-Anonymity: A Model for Protecting Privacy , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[15]  Yiannis S. Boutalis,et al.  FCTH: Fuzzy Color and Texture Histogram - A Low Level Feature for Accurate Image Retrieval , 2008, 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services.

[16]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[17]  Thomas S. Huang,et al.  Relevance feedback in image retrieval: A comprehensive review , 2003, Multimedia Systems.

[18]  Jun Zhang,et al.  Content Based Image Retrieval Using Unclean Positive Examples , 2009, IEEE Transactions on Image Processing.

[19]  João Leitão,et al.  Privacy-Preserving Content-Based Image Retrieval in the Cloud , 2014, 2015 IEEE 34th Symposium on Reliable Distributed Systems (SRDS).

[20]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[21]  Mathias Lux,et al.  Lire: lucene image retrieval: an extensible java CBIR library , 2008, ACM Multimedia.

[22]  K. Srinathan,et al.  Private Content Based Image Retrieval , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Anil K. Jain.  Data clustering: 50 years beyond K-means , 2008, Pattern Recognit. Lett..

[24]  Laurent Amsaleg,et al.  A Privacy-Preserving Framework for Large-Scale Content-Based Information Retrieval , 2015, IEEE Transactions on Information Forensics and Security.

[25]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Thomas Sikora,et al.  The MPEG-7 visual standard for content description-an overview , 2001, IEEE Trans. Circuits Syst. Video Technol..

[27]  Carla E. Brodley,et al.  Identifying Mislabeled Training Data , 1999, J. Artif. Intell. Res..

[28]  Min Wu,et al.  Enabling search over encrypted multimedia databases , 2009, Electronic Imaging.

[29]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[30]  James Ze Wang,et al.  Content-based image retrieval: approaches and trends of the new age , 2005, MIR '05.

[31]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  Shih-Fu Chang,et al.  Image Retrieval: Current Techniques, Promising Directions, and Open Issues , 1999, J. Vis. Commun. Image Represent..

[33]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[34]  Yunhao Liu,et al.  PIC: Enable Large-Scale Privacy Preserving Content-Based Image Search on Cloud , 2015, IEEE Transactions on Parallel and Distributed Systems.

[35]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.