By Hisham Al-Assam, Ali Abboud, Harin Sellahewa (auth.), Yun Q. Shi, Stefan Katzenbeisser (eds.)
Since the mid Nineties, information hiding has been proposed as an permitting expertise for securing multimedia communique, and is now utilized in a number of functions together with broadcast tracking, motion picture fingerprinting, steganography, video indexing and retrieval, and picture authentication. information hiding and cryptographic recommendations are frequently mixed to enrich one another, hence triggering the improvement of a brand new study box of multimedia safety. in addition to, similar disciplines, steganalysis and knowledge forensics, are more and more attracting researchers and changing into one other new learn box of multimedia defense. This magazine, LNCS Transactions on info Hiding and Multimedia defense, goals to be a discussion board for all researchers in those rising fields, publishing either unique and archival learn effects.
This particular factor includes 5 chosen papers that have been offered on the Workshop on development popularity for IT defense, held in Darmstadt, Germany, in September 2010, along with the thirty second Annual Symposium of the German organization for development popularity, DAGM 2010. It demonstrates the wide variety of security-related themes that make the most of graphical information. The contributions discover the protection and reliability of biometric information, the ability of laptop studying how to differentiate solid pictures from originals, the effectiveness of recent watermark embedding schemes and using details fusion in steganalysis.
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Additional info for Transactions on Data Hiding and Multimedia Security VIII: Special Issue on Pattern Recognition for IT Security
The helper data s is built as a difference between reference vector w and corresponding codeword ci. In order to reproduce the codeword ci from the test vector w’, the sketch value s is subtracted and the nearest neighbor in the codeword space is returned. The reference vector w can be reconstructed by addition of s. In order to show the direct conformity of the fuzzy extractor and the fuzzy commitment scheme, the generation procedure of the fuzzy extractor is given as following, whereby SS corresponds to the Secure Sketch Generation procedure, Ext is the Strong Extractor, Rec is the Reconstruction procedure and h refers to an arbitrary cryptographic hash function: Randomly choose i: s = SS ( w) = w − c i R = Ext ( w) = h ( w) The Reproduction procedure can be presented as following: R = Ext ( Rec ( w' , s )) = h ( Rec ( w' , s )) = h ((arg min k (( w'− s ), c k ) + s ) = h(c i + s ) = h ( w) The fuzzy extractor was originally introduced for discrete signals.
Our investigation relies on the work of Guyon et al. . Both filter and wrapper approaches are studied. For filter-based selection, the feature ranking is done using the correlation coefficient or mutual information. The wrappers are represented by nested subset selection with forward or backward selection or with multiplicative updates and subsequent classification. Several works bring up a feature selection issue in biometric domain. For instance, Kumar et al. in  used the Correlation Based Feature Selection (CFS) swiped from Hall et al.
According to Guyon in  “Fisher’s criterion to rank variables in a classification problem where the covariance matrix is diagonal is optimum for Fisher’s linear discriminant classifier”. Even though filters do not usually lead to optimal classification performance, they are preferred due to their computational efficiency. Clearly, the quality criterion choice (in other words the appropriate heuristic) is the most critical issue. In this work several heuristics for filter-based feature selection are evaluated, as well as three editions of wrapper-based feature selection.
Transactions on Data Hiding and Multimedia Security VIII: Special Issue on Pattern Recognition for IT Security by Hisham Al-Assam, Ali Abboud, Harin Sellahewa (auth.), Yun Q. Shi, Stefan Katzenbeisser (eds.)