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Conception and limits of robust perceptual hashing: toward side information assisted hash functions
 in Proceedings of SPIE Photonics West, Electronic Imaging / Media Forensics and Security XI
, 2009
"... In this paper, we consider some basic concepts behind the design of existing robust perceptual hashing techniques for content identification. We show the limits of robust hashing from the communication perspectives as well as propose an approach capable to overcome these shortcomings in certain setu ..."
Abstract

Cited by 14 (13 self)
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In this paper, we consider some basic concepts behind the design of existing robust perceptual hashing techniques for content identification. We show the limits of robust hashing from the communication perspectives as well as propose an approach capable to overcome these shortcomings in certain setups. The consideration is based on both achievable rate and probability of error. We use a fact that most of robust hashing algorithms are based on dimensionality reduction using random projections and quantization. Therefore, we demonstrate the corresponding achievable rate and probability of error based on the random projections and compare with the results for the direct domain. The effect of dimensionality reduction is studied and the corresponding approximations are provided based on JohnsonLindenstrauss lemma. A side information assisted robust perceptual hashing is proposed as a solution to the above shortcomings. Notations: We use capital letters to denote scalar random variables X and X to denote vector random variables, corresponding small letters x and x to denote the realizations of scalar and vector random variables, respectively. All vectors without sign tilde are assumed to be of the length N and with the sign tilde of length L with the corresponding subindexes. The binary representation of vectors will be denoted as bx with the corresponding subindexing. We use X ∼ pX(x) or simply X ∼ p(x) to indicate that a random variable X is distributed according to pX(x). N(µ,σ2 X) stands for Gaussian distribution with mean µ and variance σ2 X. . denotes Euclidean vector norm and Q(.) stands for Qfunction.
Privacy Preserving Search of Multimedia 1
"... The advancement of information technology is rapidly integrating the physical world where we live and the online world where we retrieve and share information. One immediate example of such integration is the increasing popularity of storing and managing personal data using thirdparty web services, ..."
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The advancement of information technology is rapidly integrating the physical world where we live and the online world where we retrieve and share information. One immediate example of such integration is the increasing popularity of storing and managing personal data using thirdparty web services, as part of the emerging trend of cloud computing. Secure management of sensitive data stored online is becoming one of the critical research issues in cloud computing and online privacy protection. In this paper, we propose techniques to achieve content based multimedia retrieval over encrypted databases, which can be used for online management of multimedia data while preserving data privacy. We propose two types of secure retrieval schemes by combining cryptographic techniques, such as order preserving encryption and randomized hash functions, with image processing and information retrieval techniques, such as visual words representation, inverted index, and minhash. The first type of retrieval schemes scramble visual features extracted from images and allow similarity comparison of the features in their encrypted forms. The second type of schemes encrypt the stateoftheart search indexes without significantly affecting their search capability. The two types of schemes are complementary and represent different tradeoffs between userside computational complexity and communication overhead. Retrieval results on an encrypted color image database and security analysis under different attack models show that retrieval performance comparable to conventional plaintext retrieval schemes can be achieved over encrypted databases while ensuring data confidentiality. Index Terms: Content based image retrieval, secure search, secure cloud computing, visual words, minhash, random projection, order preserving encryption I.
Research Article An Extended Image Hashing Concept: ContentBased Fingerprinting Using FJLT
"... Dimension reduction techniques, such as singular value decomposition (SVD) and nonnegative matrix factorization (NMF), have been successfully applied in image hashing by retaining the essential features of the original image matrix. However, a concern of great importance in image hashing is that no ..."
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Dimension reduction techniques, such as singular value decomposition (SVD) and nonnegative matrix factorization (NMF), have been successfully applied in image hashing by retaining the essential features of the original image matrix. However, a concern of great importance in image hashing is that no single solution is optimal and robust against all types of attacks. The contribution of this paper is threefold. First, we introduce a recently proposed dimension reduction technique, referred as Fast JohnsonLindenstrauss Transform (FJLT), and propose the use of FJLT for image hashing. FJLT shares the low distortion characteristics of a random projection, but requires much lower computational complexity. Secondly, we incorporate FourierMellin transform into FJLT hashing to improve its performance under rotation attacks. Thirdly, we propose a new concept, namely, contentbased fingerprint, as an extension of image hashing by combining different hashes. Such a combined approach is capable of tackling all types of attacks and thus can yield a better overall performance in multimedia identification. To demonstrate the superior performance of the proposed schemes, receiver operating characteristics analysis over a large image database and a large class of distortions is performed and compared with the stateoftheart image hashing using NMF. Copyright © 2009 X. Lv and Z. J. Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1.