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Algorithms
Special Issues
Machine Learning Models and Algorithms for Image Processing
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Machine Learning Models and Algorithms for Image Processing

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 724

Special Issue Editors

Dr. Marcin Iwanowski

E-Mail Website
Guest Editor
Institute of Control and Industrial Electronics, Warsaw University of Technology, 00-662 Warszawa, Poland
Interests: computer vision; image processing; machine learning; deep learning; artificial intelligence
Prof. Dr. Bogusław Cyganek

E-Mail Website
Guest Editor
Faculty of Computer Science, Electronics and Telecommunication, Department of Electronics, AGH University of Science and Technology, 30-059 Krakow, Poland
Interests: computer vision; machine learning; artificial intelligence; software development in C++ and Python
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to submit a paper to the Special Issue of Algorithms entitled “Machine Learning Models and Algorithms for Image Processing”. Applying machine learning algorithms to image processing is currently experiencing intense development. We are looking for papers in this field, reporting new and innovative approaches and in-depth surveys. High-quality papers are solicited to address both theoretical and practical issues of vision-related machine learning. Submissions are welcome both for theoretical development problems as well as applications.

Potential topics include, but are not limited to, object detection, semantic and instance segmentation, image description, deep learning models in computer vision, medical imaging, remote sensing, machine vision, robot vision, underwater image analysis and enhancement, sonar image processing, thermal imaging and object detection/classification in near and far thermal images, image fusion, multi-modal data retrieval, video analysis, anomaly detection, tensor processing for multimedia, driver assisting/monitoring systems, video surveillance, image and video processing for building/construction inspection, image and video in industry, and many more.

Dr. Marcin Iwanowski
Prof. Dr. Bogusław Cyganek
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. Algorithms 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.

Keywords

  • machine learning
  • deep learning
  • computer vision
  • image processing
  • object detection
  • semantic and instance segmentation
  • image description
  • medical imaging
  • remote sensing
  • machine vision
  • robot vision
  • video surveillance
  • anomaly detection
  • underwater image enhancement
  • sonar imaging
  • thermal imaging
  • spectra fusion
  • multi-modal data retrieval
  • tensor processing
  • imaging in industry

Published Papers (1 paper)

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Research

23 pages, 6574 KiB  
Article
Sub-Band Backdoor Attack in Remote Sensing Imagery
by Kazi Aminul Islam, Hongyi Wu, Chunsheng Xin, Rui Ning, Liuwan Zhu and Jiang Li
Algorithms 2024, 17(5), 182; https://doi.org/10.3390/a17050182 - 28 Apr 2024
Viewed by 497
Abstract
Remote sensing datasets usually have a wide range of spatial and spectral resolutions. They provide unique advantages in surveillance systems, and many government organizations use remote sensing multispectral imagery to monitor security-critical infrastructures or targets. Artificial Intelligence (AI) has advanced rapidly in recent [...] Read more.
Remote sensing datasets usually have a wide range of spatial and spectral resolutions. They provide unique advantages in surveillance systems, and many government organizations use remote sensing multispectral imagery to monitor security-critical infrastructures or targets. Artificial Intelligence (AI) has advanced rapidly in recent years and has been widely applied to remote image analysis, achieving state-of-the-art (SOTA) performance. However, AI models are vulnerable and can be easily deceived or poisoned. A malicious user may poison an AI model by creating a stealthy backdoor. A backdoored AI model performs well on clean data but behaves abnormally when a planted trigger appears in the data. Backdoor attacks have been extensively studied in machine learning-based computer vision applications with natural images. However, much less research has been conducted on remote sensing imagery, which typically consists of many more bands in addition to the red, green, and blue bands found in natural images. In this paper, we first extensively studied a popular backdoor attack, BadNets, applied to a remote sensing dataset, where the trigger was planted in all of the bands in the data. Our results showed that SOTA defense mechanisms, including Neural Cleanse, TABOR, Activation Clustering, Fine-Pruning, GangSweep, Strip, DeepInspect, and Pixel Backdoor, had difficulties detecting and mitigating the backdoor attack. We then proposed an explainable AI-guided backdoor attack specifically for remote sensing imagery by placing triggers in the image sub-bands. Our proposed attack model even poses stronger challenges to these SOTA defense mechanisms, and no method was able to defend it. These results send an alarming message about the catastrophic effects the backdoor attacks may have on satellite imagery. Full article
(This article belongs to the Special Issue Machine Learning Models and Algorithms for Image Processing)
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