Transactions on Data Hiding and Multimedia Security VIII [electronic resource] /

Exploiting Relative Entropy and Quality Analysis in Cumulative Partial Biometric Fusion -- Improving Reliability of Biometric Hash Generation through the Selection of Dynamic Handwriting Features. Feature-Based Forensic Camera Model Identification -- Watermark Embedding Using Audio Fingerprinting -- Plausibility Considerations on Steganalysis as a Security Mechanism – Discussions on the Example of Audio Steganalysis. Improving Reliability of Biometric Hash Generation through the Selection of Dynamic Handwriting Features. Feature-Based Forensic Camera Model Identification -- Watermark Embedding Using Audio Fingerprinting -- Plausibility Considerations on Steganalysis as a Security Mechanism – Discussions on the Example of Audio Steganalysis.

Since the mid 1990s, data hiding has been proposed as an enabling technology for securing multimedia communication, and is now used in various applications including broadcast monitoring, movie fingerprinting, steganography, video indexing and retrieval, and image authentication. Data hiding and cryptographic techniques are often combined to complement each other, thus triggering the development of a new research field of multimedia security. Besides, two related disciplines, steganalysis and data forensics, are increasingly attracting researchers and becoming another new research field of multimedia security. This journal, LNCS Transactions on Data Hiding and Multimedia Security, aims to be a forum for all researchers in these emerging fields, publishing both original and archival research results. This special issue contains five selected papers that were presented at the Workshop on Pattern Recognition for IT Security, held in Darmstadt, Germany, in September 2010, in conjunction with the 32nd Annual Symposium of the German Association for Pattern Recognition, DAGM 2010. It demonstrates the broad range of security-related topics that utilize graphical data. The contributions explore the security and reliability of biometric data, the power of machine learning methods to differentiate forged images from originals, the effectiveness of modern watermark embedding schemes and the use of information fusion in steganalysis.