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Deep Learning Based Techniques for Multimedia Systems

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 May 2023) | Viewed by 9790

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Special Issue Editors

Dr. Ben Soh

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Guest Editor

Department of Computer Science and IT, La Trobe University, Bundoora, VIC 3068, Australia
Interests: fault-tolerant and secure computing; cloud computing; information systems research; pervasive wireless network communications; business process management

Dr. Singara Singh Kasana

E-Mail Website
Guest Editor

Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala 147004, India
Interests: image compression; image enhancement; image authentication and protection; video forgery detection; hyperspectral imaging; machine learning

Dr. Geeta Kasana

E-Mail Website
Guest Editor

Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala 147004, India
Interests: image processing; information security; image authentication; watermarking; steganography

Special Issue Information

Dear Colleagues,

Cyberattacks are specifically intended to steal an enterprise’s customer data, sales data, intellectual property documents, source codes, and software keys. Cybersecurity systems may use deep learning to analyze patterns and learn from them in order to prevent similar attacks and respond to changing behavior. They can enable cybersecurity teams to be more proactive in mitigating threats and responding in real time to active attacks.

The aim of this Special Issue is to address security concerns for various multimedia systems. Multimedia data include a broad variety ranging from images, documents, and audios to all kinds of videos. We foresee contributions focusing on innovative deep-learning-based strategies for securing multimedia systems.

The topics of interest of the Special Issue include but are not limited to:

Deep learning for preventing exploitable vulnerabilities of Internet of Bodies (IoB) devices;
Deep-learning-based situational awareness for intrusion, spam, and social engineering detection;
Deep-learning-based secret sharing techniques;
Deep-learning-based image steganography and steganalysis;
Deep-learning-based techniques for authentication and protection of image and videos;
Deep learning for secure techniques for fingerprint;
Deep-learning-based techniques for 2D/3D face and ear biometrics;
Deep learning for biometric template protection;
Deep-learning-based image encryption and decryption techniques;
Protection and authentication of multispectral and hyperspectral images;
Digital evidence detection in computer forensics.

Dr. Ben Soh
Dr. Singara Singh Kasana
Dr. Geeta Kasana
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 submissions that pass pre-check are 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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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.

Keywords

deep learning
Internet of Bodies
steganography
biometric protection
encryption
evidence detection
multispectral and hyperspectral image protection

Published Papers (4 papers)

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Research

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24 pages, 17754 KiB

Open AccessArticle

Building Façade Style Classification from UAV Imagery Using a Pareto-Optimized Deep Learning Network

by Rytis Maskeliūnas, Andrius Katkevičius, Darius Plonis, Tomyslav Sledevič, Adas Meškėnas and Robertas Damaševičius

Electronics 2022, 11(21), 3450; https://doi.org/10.3390/electronics11213450 - 25 Oct 2022

Cited by 4 | Viewed by 2082

Abstract

The article focuses on utilizing unmanned aerial vehicles (UAV) to capture and classify building façades of various forms of cultural sites and structures. We propose a Pareto-optimized deep learning algorithm for building detection and classification in a congested urban environment. Outdoor image processing becomes difficult in typical European metropolitan situations due to dynamically changing weather conditions as well as various objects obscuring perspectives (wires, overhangs, posts, other building parts, etc.), therefore, we also investigated the influence of such ambient “noise”. The approach was tested on 8768 UAV photographs shot at different angles and aimed at very different 611 buildings in the city of Vilnius (Wilno). The total accuracy was 98.41% in clear view settings, 88.11% in rain, and 82.95% when the picture was partially blocked by other objects and in the shadows. The algorithm’s robustness was also tested on the Harward UAV dataset containing images of buildings taken from above (roofs) while our approach was trained using images taken at an angle (façade still visible). Our approach was still able to achieve acceptable 88.6% accuracy in building detection, yet the network showed lower accuracy when assigning the correct façade class as images lacked necessary façade information. Full article

(This article belongs to the Special Issue Deep Learning Based Techniques for Multimedia Systems)

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11 pages, 4412 KiB

Open AccessCommunication

Concrete Bridge Crack Image Classification Using Histograms of Oriented Gradients, Uniform Local Binary Patterns, and Kernel Principal Component Analysis

by Hajar Zoubir, Mustapha Rguig, Mohamed El Aroussi, Abdellah Chehri and Rachid Saadane

Electronics 2022, 11(20), 3357; https://doi.org/10.3390/electronics11203357 - 18 Oct 2022

Cited by 6 | Viewed by 1721

Abstract

Bridges deteriorate over time, which requires the continuous monitoring of their condition. There are many digital technologies for inspecting and monitoring bridges in real-time. In this context, computer vision has extensively studied cracks to automate their identification in concrete surfaces, overcoming the conventional manual methods that rely on human judgment. The general framework of vision-based techniques consists of feature extraction using different filters and descriptors and classifier training to perform the classification task. However, training can be time-consuming and computationally expensive, depending on the dimension of the features. To address this limitation, dimensionality reduction techniques are applied to extracted features, and a new feature subspace is generated. This work used histograms of oriented gradients (HOGs) and uniform local binary patterns (ULBPs) to extract features from a dataset containing over 3000 uncracked and cracked images covering different patterns of cracks and concrete surface representations. Nonlinear dimensionality reduction was performed using kernel principal component analysis (KPCA), and three machine learning classifiers were implemented to conduct the classification. The experimental results show that the classification scheme based on the support-vector machine (SVM) model and feature-level fusion of the HOG and ULBP features after KPCA application provided the best results as an accuracy of 99.26% was achieved by the proposed classification framework. Full article

(This article belongs to the Special Issue Deep Learning Based Techniques for Multimedia Systems)

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19 pages, 3850 KiB

Open AccessArticle

A Big Data Approach for Demand Response Management in Smart Grid Using the Prophet Model

by Sanju Kumari, Neeraj Kumar and Prashant Singh Rana

Electronics 2022, 11(14), 2179; https://doi.org/10.3390/electronics11142179 - 12 Jul 2022

Cited by 2 | Viewed by 1706

Abstract

Smart Grids (SG) generate extensive data sets regarding the system variables, viz., and demand and supply. These extremely large data sets are known as big data. Hence, preprocessing of this vast data and integration become critical steps in the load forecasting process. The precise prediction of the load is the primary concern while balancing the demand and supply in SG. Many techniques were devised for load forecasting using machine learning methods such as Deep-learning Models. However, in the case of large data sets, only a few models provide good performance, viz. Autoregressive Integrated Moving Average (ARIMA). However, this approach is complex, as it takes a minimum of 50 observations to make an evaluation. In this paper, the Prophet technique is used in the prediction of future demand response based on the past data, which is in the form of a time series. This technique is valid even if a few values in the time series are not available. Furthermore, the procedure is not affected by fluctuations, trends, and abnormal variations. The automatic model fitting approach is adopted for its effective performance. Further, ARIMA and Prophet model have been used to forecast and the approach is verified using various evaluation metrics. The demand response management was achieved and is being validated with two data sets. The results show the effectiveness of the Prophet model in the demand response management scheme involving large data sets. Full article

(This article belongs to the Special Issue Deep Learning Based Techniques for Multimedia Systems)

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Review

Jump to: Research

34 pages, 1749 KiB

Open AccessReview

Machine Learning and Deep Learning Techniques for Spectral Spatial Classification of Hyperspectral Images: A Comprehensive Survey

by Reaya Grewal, Singara Singh Kasana and Geeta Kasana

Electronics 2023, 12(3), 488; https://doi.org/10.3390/electronics12030488 - 17 Jan 2023

Cited by 6 | Viewed by 3291

Abstract

The growth of Hyperspectral Image (HSI) analysis is due to technology advancements that enable cameras to collect hundreds of continuous spectral information of each pixel in an image. HSI classification is challenging due to the large number of redundant spectral bands, limited training samples and non-linear relationship between the collected spatial position and the spectral bands. Our survey highlights recent research in HSI classification using traditional Machine Learning techniques like kernel-based learning, Support Vector Machines, Dimension Reduction and Transform-based techniques. Our study also digs into Deep Learning (DL) techniques that involve the usage of Autoencoders, 1D, 2D and 3D-Convolutional Neural Networks to classify HSI. From the comparison, it is observed that DL-based classification techniques outperform ML-based techniques. It has also been observed that spectral-spatial HSI classification outperforms pixel-by-pixel classification because it incorporates spectral signatures and spatial domain information. The performance of ML and DL-based classification techniques has been reviewed on commonly used land cover datasets like Indian Pines, Salinas valley and Pavia University. Full article

(This article belongs to the Special Issue Deep Learning Based Techniques for Multimedia Systems)

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