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Towards Adversarial Machine Learning and Defenses in Sensors Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 4606
Please contact the Guest Editor or the Section Managing Editor at (ava.jiang@mdpi.com) for any queries.

Special Issue Editor


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Guest Editor
Department of Media Design and TechnologyFaculty of Engineering and InformaticsUniversity of Bradford, Bradford, UK
Interests: visual surveillance; information mining; data encryption

Special Issue Information

Dear Colleagues,

Machine Learning has seen immense growth in recent decades across a broad spectrum of applications. The emergence and evolution of deep learning have further improved the performance of many systems. The deep learning models’ evolution has a great impact on computer vision systems, medical imaging, speech recognition, robotics, and self-driving cars. The deep learning models provide excellent performance and reliability due to its ability to generalize for many fields and to identify underlying patterns and make future predictions. The main research areas for deep learning in computer vision, due to its excellent performance, is in surveillance and biometric verification systems. The emergence of adversarial machine learning provides a new direction to the machine learning field where limited data is available. The development of the Generative Adversarial Network (GAN) provides artificial data, which is usually difficult to judge even from the human eye. The artificial data generated by GAN is used for both good and bad purposes.

The ability of deep learning models to observe and recognize patterns makes them valuable in the field of computer vision and image processing. However, deep learning models can be fooled by artificial (fake) data. It has been observed that slight modifications in the images can cause deep learning models to make wrong predictions. These wrong classifications or predictions prove expensive, especially for biometric recognition including speech, face, iris, gait recognition, and autonomous vehicles. These issues make deep learning vulnerable to adversarial attacks. Therefore, there is a strong need to design defenses against such adversarial attacks and this area needs more investigation and research.

This Special Issue aims to provide a forum for individuals from academia and industry to present their novel ideas in the field of adversarial machine learning, GANs, adversarial examples, adversarial defenses, image anonymity, and image forensics. The anticipated research should be industry and application-oriented to handle important issues. Original technical research articles including review papers are invited.

Dr. Irfan Mehmood
Guest Editor

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Keywords

  • Digital image processing
  • Image analysis
  • Pattern recognition
  • Video processing
  • Multi-dimension signal processing
  • Computer vision
  • GANs based model for image processing
  • Sensors applications
  • Sensing techniques
  • Design of new adversarial examples
  • Design and development of defense against adversarial examples
  • Transferability of adversarial examples
  • Robust models against biometric verification attacks
  • Theoretical models and analysis for adversarial attacks
  • Optimization techniques to train models
  • Multimodal adversarial attacks and defenses

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Published Papers (1 paper)

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Review

29 pages, 791 KiB  
Review
Study of Different Deep Learning Methods for Coronavirus (COVID-19) Pandemic: Taxonomy, Survey and Insights
by Lamia Awassa, Imen Jdey, Habib Dhahri, Ghazala Hcini, Awais Mahmood, Esam Othman and Muhammad Haneef
Sensors 2022, 22(5), 1890; https://doi.org/10.3390/s22051890 - 28 Feb 2022
Cited by 20 | Viewed by 3841
Abstract
COVID-19 has evolved into one of the most severe and acute illnesses. The number of deaths continues to climb despite the development of vaccines and new strains of the virus have appeared. The early and precise recognition of COVID-19 are key in viably [...] Read more.
COVID-19 has evolved into one of the most severe and acute illnesses. The number of deaths continues to climb despite the development of vaccines and new strains of the virus have appeared. The early and precise recognition of COVID-19 are key in viably treating patients and containing the pandemic on the whole. Deep learning technology has been shown to be a significant tool in diagnosing COVID-19 and in assisting radiologists to detect anomalies and numerous diseases during this epidemic. This research seeks to provide an overview of novel deep learning-based applications for medical imaging modalities, computer tomography (CT) and chest X-rays (CXR), for the detection and classification COVID-19. First, we give an overview of the taxonomy of medical imaging and present a summary of types of deep learning (DL) methods. Then, utilizing deep learning techniques, we present an overview of systems created for COVID-19 detection and classification. We also give a rundown of the most well-known databases used to train these networks. Finally, we explore the challenges of using deep learning algorithms to detect COVID-19, as well as future research prospects in this field. Full article
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