Special Issue "Information-Theoretic Methods for Deep Learning Based Data Acquisition, Analysis and Security"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: 31 August 2020.

Special Issue Editors

Prof. Dr. Slava Voloshynovskiy
E-Mail Website
Guest Editor
Stochastic Information Processing Group, Department of Computer Science, Faculty of Science, University of Geneva, Switzerland
Interests: information-theoretic machine learning; image processing; security and privacy
Dr. Benedetta Tondi
E-Mail Website
Guest Editor
Visual Information Processing and Protection Group, Department of Information Engineering and Mathematics, University of Siena, Italy
Interests: adversarial machine learning; information theory; adversarial signal processing; multimedia forensics

Special Issue Information

Dear Colleagues,

The recent advancement of machine learning techniques in general and deep learning (DL) in particular recalls a necessity to carefully rethink many traditional approaches in data acquisition, analysis, processing, and security. At the same time, deep learning, as a glorified signal processing tool, lacks a solid information-theoretical basis and strong connections with the fundamental information-theoretic results in channel and source coding, hypothesis testing, estimation, and security. The goal of this Special Issue is to link deep learning techniques with information theory and thus create a basis for theoretically explainable machine learning and interpretable deep learning solutions.

We would like to welcome original works addressing the following:

  • New methods for data acquisition, including sampling, when the acquisition/imaging operator is optimized on the statistics of training data and deep reconstruction algorithms (e.g., DL for learning optimized generational methods, DL-based adaptation of sensor planning for high resolution image acquisition and reconstruction in sensor networks); a special interest is in new approaches establishing the fundamental links between information-theoretic limits and characteristics of imaging systems coupled with reconstruction algorithms considered as optimized encoder–decoder pairs. Contributions are welcome to cover the broad spectrum of problems, such as restoration from blurred images and videos, reconstruction from sparsely sampled data in transform domains, demosaicing, super-resolution, and inpainting, etc;
  • New methods of data analysis bridging information-theoretic methods with deep learning systems and promoting explainable machine learning, especially in unsupervised settings; this concerns the information-theoretic analysis of learned features and filters. One of the main goals is to better characterize the impact of the training data and clearly understand which portion of training data contributes to the success of the derived solutions. Possible topics include the exploration of the link between DL and variational inference, the information bottleneck paradigm, and anomaly detection;
  • New approaches to secure machine learning to reduce vulnerability to adversarial attacks, thus bridging information-theoretic and cryptographic principles with machine learning; in this way, it is possible to characterize improved security in terms of an ‘information’ advantage of the defender over the attacker. Particular interest is also devoted to approaches pertaining to the development of information theoretical methods to measure the vulnerability of systems to attack;
  • New techniques for privacy preserving learning extending the limits of current centralized architectures (based on a single classifier having the access to the training data from all classes) to distributed and federated architectures with a partial and limited access to training data.

This Special Issue should serve as a platform for multi-disciplinary researchers interested in sharing their results with other communities using similar techniques. All submitted manuscripts will be subject to peer review, and accepted papers will be available via open access. We welcome the submission of extended conference papers with a clear justification of all extensions with respect to previously published works.

Prof. Dr. Slava Voloshynovskiy
Dr. Benedetta Tondi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • information theory
  • deep learning
  • data acquisition and reconstruction
  • data analysis
  • data estimation and inference
  • classification
  • security and privacy

Published Papers (1 paper)

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Open AccessFeature PaperArticle
Are Classification Deep Neural Networks Good for Blind Image Watermarking?
Entropy 2020, 22(2), 198; https://doi.org/10.3390/e22020198 - 08 Feb 2020
Image watermarking is usually decomposed into three steps: (i) a feature vector is extracted from an image; (ii) it is modified to embed the watermark; (iii) and it is projected back into the image space while avoiding the creation of visual artefacts. This [...] Read more.
Image watermarking is usually decomposed into three steps: (i) a feature vector is extracted from an image; (ii) it is modified to embed the watermark; (iii) and it is projected back into the image space while avoiding the creation of visual artefacts. This feature extraction is usually based on a classical image representation given by the Discrete Wavelet Transform or the Discrete Cosine Transform for instance. These transformations require very accurate synchronisation between the embedding and the detection and usually rely on various registration mechanisms for that purpose. This paper investigates a new family of transformation based on Deep Neural Networks trained with supervision for a classification task. Motivations come from the Computer Vision literature, which has demonstrated the robustness of these features against light geometric distortions. Also, adversarial sample literature provides means to implement the inverse transform needed in the third step above mentioned. As far as zero-bit watermarking is concerned, this paper shows that this approach is feasible as it yields a good quality of the watermarked images and an intrinsic robustness. We also tests more advanced tools from Computer Vision such as aggregation schemes with weak geometry and retraining with a dataset augmented with classical image processing attacks. Full article
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