EURASIP Journal on Information Security
Dependable Deep Learning for Security-Oriented Applications
June 15, 2019 (closed)
In the last decades, a large number of techniques have been proposed to ensure integrity and authenticity of data in security-oriented applications, e.g. multimedia forensics, biometrics, watermarking and information hiding, network intrusion detection, reputation systems, etc.... The development of these methods have received a new boost in the last few years with the advent of Deep Learning (DL) techniques and Convolutional Neural Networks (CNNs) and, in most cases, the performance of DL-based methods greatly exceed those achieved by classical model-based and standard machine learning approaches. The dependability of these techniques, however, is questionable due to a number of shortcomings, some of which are particularly relevant in security-related applications. This Special Issue aims at providing a venue for research investigating strengths and limits of DL-based tools for security-related applications, and proposing more advanced and powerful tools that go beyond the state-of-the-art in the field.
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.