The overall goal of the EURASIP Journal on Information Security, sponsored by the European Association for Signal Processing (EURASIP), is to bring together researchers and practitioners dealing with the general field of information security, with a particular emphasis on the use of signal processing tools in adversarial environments. As such, it addresses all works whereby security is achieved through a combination of techniques from cryptography, computer security, machine learning and multimedia signal processing. Application domains lie, for example, in secure storage, retrieval and tracking of multimedia data, secure outsourcing of computations, forgery detection of multimedia data, or secure use of biometrics. The journal also welcomes survey papers that give the reader a gentle introduction to one of the topics covered as well as papers that report large-scale experimental evaluations of existing techniques. Pure cryptographic papers are outside the scope of the journal.

This journal has moved from Hindawi to Springer in March 2011 and is published now in its SpringerOpen portfolio of open access journals.

The EURASIP Journal on Information Security is published using an open access publishing model, which makes the full-text of all peer-reviewed articles freely available online with no subscription barriers. EURASIP Journal on Information Security employs a paperless, electronic submission and evaluation system to promote a rapid turnaround in the peer review process. Fairness and transparency of the review process are pursued by traditional and innovative means.

In addition to creating an international forum for the publication of high quality articles in the broad range of information security, the EURASIP Journal on Information Security aims at reaching the highest quality standards with regard to the experimental section of published articles. To this aim we strongly encourage authors to describe, whenever present, experimental results in a way that they can easily be reproduced. For example, algorithms proposed or tested within the papers should be accompanied by a detailed pseudo-code or block diagram description, all necessary parameters should be listed, and a detailed description of the data set used for training or performance evaluation should be given. At the same time, it is recommended that the authors share the source code of the algorithms with readers and reviewers. To this aim specific tools are made available by the electronic submission procedure to upload any relevant piece of the software together with the manuscript. If the authors choose this option, the source code will be freely available to readers under the Creative Commons License after the paper is accepted and published, which allows the free re-use of the source code for all non-commercial purposes.

In order to further facilitate the verification of results and the comparison among competing schemes, the journal also seeks articles whose goal is that of comparing existing systems, testing existing algorithms against new data sets, reporting experimental evidence that results published by someone else are wrong (in this last case software sharing is a mandatory requirement).

Topics relevant to the journal include, but are not limited to:

·    Multimedia security primitives (such digital watermarking, perceptual hashing, multimedia authentictaion)
·    Steganography and Steganalysis
·    Fingerprinting and traitor tracing
·    Joint signal processing and encryption, signal processing in the encrypted domain, applied cryptography
·    Biometrics (fusion, multimodal biometrics, protocols, security issues)
·    Digital forensics
·    Multimedia signal processing approaches tailored towards adversarial environments
·    Machine learning in adversarial environments
·    Digital Rights Management
·    Network security (such as physical layer security, intrusion detection)
·    Hardware security, Physical Unclonable Functions
·    Privacy-Enhancing Technologies for multimedia data
·    Private data analysis, security in outsourced computations, cloud privacy