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In the last decade, a large number of techniques have been proposed to ensure integrity and authenticity of data in security-oriented applications, e.g. multime-dia forensics, biometrics, watermarking and information hiding, network intrusion detection, reputation systems, etc.... The development of these methods has re-ceived a new boost in the last few years with the advent of Deep Learning (DL) techniques and Convolutional Neural Networks (CNNs). In most cases, the per-formance of DL-based methods greatly exceed those achieved by classical mo-del-based and standard machine learning approaches. Despite the initial gust of optimism brought by the advent of DL tools, DL-based approaches suffer from a number of shortcomings, many of which are particularly relevant in security-oriented applications, such as: the need for a huge amount of data representati-ve of all the real word situations (that cannot all be foreseen at training time); the poor robustness to adversarial attacks; the difficulty of getting properly trained models which capture discriminative features without relying on confounding factors; etc.…
This Special Issue aims at providing a venue for research investigating strengths and limits of DL-based tools for security-related applications, and proposing mo-re advanced and powerful tools that overcome the state-of-the-art in the field.
The list of topics include but is not limited to:
• Adversarial Deep Learning,
• Adversarial examples in generative models
• DL understandability
• Secure DL classification/learning
• DL for multimedia forensics and counter-forensics
• Biometrics and authentication based on DL
• DL for watermarking and information hiding
• DL for steganography and steganalysis
• DL for network intrusion detect
Deadline for submissions: 15 June 2019
Manuscripts can be submitted until the deadline. The revision process will start upon submission of the manuscript. Accepted papers will be published continuously in the journal (as soon as accepted).
Lead Guest Editor: Benedetta Tondi, University of Siena, Italy
Mauro Barni, University of Siena, Italy.
Benedetta Tondi, University of Siena, Italy
Slava Voloshynovskiy, University of Geneva, Switzerland
More detailed information is available in the official Call for Papers.
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