IEEE Trans Image Process - Robust video hashing via multilinear subspace projections.

Tópicos

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{ imag(1947) propos(1133) code(1026) }
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Resumo

The goal of video hashing is to design hash functions that summarize videos by short fingerprints or hashes. While traditional applications of video hashing lie in database searches and content authentication, the emergence of websites such as YouTube and DailyMotion poses a challenging problem of anti-piracy video search. That is, hashes or fingerprints of an original video (provided to YouTube by the content owner) must be matched against those uploaded to YouTube by users to identify instances of "illegal" or undesirable uploads. Because the uploaded videos invariably differ from the original in their digital representation (owing to incidental or malicious distortions), robust video hashes are desired. We model videos as order-3 tensors and use multilinear subspace projections, such as a reduced rank parallel factor analysis (PARAFAC) to construct video hashes. We observe that, unlike most standard descriptors of video content, tensor-based subspace projections can offer excellent robustness while effectively capturing the spatio-temporal essence of the video for discriminability. We introduce randomization in the hash function by dividing the video into (secret key based) pseudo-randomly selected overlapping sub-cubes to prevent against intentional guessing and forgery. Detection theoretic analysis of the proposed hash-based video identification is presented, where we derive analytical approximations for error probabilities. Remarkably, these theoretic error estimates closely mimic empirically observed error probability for our hash algorithm. Furthermore, experimental receiver operating characteristic (ROC) curves reveal that the proposed tensor-based video hash exhibits enhanced robustness against both spatial and temporal video distortions over state-of-the-art video hashing techniques.

Resumo Limpo

goal video hash design hash function summar video short fingerprint hash tradit applic video hash lie databas search content authent emerg websit youtub dailymot pose challeng problem antipiraci video search hash fingerprint origin video provid youtub content owner must match upload youtub user identifi instanc illeg undesir upload upload video invari differ origin digit represent owe incident malici distort robust video hash desir model video order tensor use multilinear subspac project reduc rank parallel factor analysi parafac construct video hash observ unlik standard descriptor video content tensorbas subspac project can offer excel robust effect captur spatiotempor essenc video discrimin introduc random hash function divid video secret key base pseudorandom select overlap subcub prevent intent guess forgeri detect theoret analysi propos hashbas video identif present deriv analyt approxim error probabl remark theoret error estim close mimic empir observ error probabl hash algorithm furthermor experiment receiv oper characterist roc curv reveal propos tensorbas video hash exhibit enhanc robust spatial tempor video distort stateoftheart video hash techniqu

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