Selected Papers from Young Researchers in Signal/Image/Video Coding and Processing, 2nd Edition

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

Deadline for manuscript submissions: closed (15 June 2025) | Viewed by 583

Special Issue Editors


E-Mail Website
Guest Editor
Tampere Handset Camera Innovation Lab, Huawei Technologies Oy (Finland) Co., Ltd., 33720 Tampere, Finland
Interests: image and video coding; lossless data compression; light field image compression; event data compression; point cloud compression; deep-learning-based quality enhancement of coding artefact removal; deep-learning-based depth estimation; deep-learning-based semantic segmentation; deep-learning-based instance segmentation; deep-learning-based image/video deblurring; hybrid coding scheme; learning-based image coding
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Tampere Handset Camera Innovation Lab, Huawei Technologies Oy (Finland) Co., Ltd., 33720 Tampere, Finland
Interests: image signal processing; computer vision; signal processing for event cameras; event camera application for computer vision; 3D sensing and applications; LiDAR; ToF camera
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The main goal of this Special Issue is to offer young researchers an opportunity to publish on an open access platform their most recent research breakthroughs in the field of computer science and engineering.

This Special Issue aims to gather novel approaches from various research fields in image/video coding and processing and computer vision. Topics of interest include but are not limited to:

  • Image and video coding;
  • Light field image compression;
  • Event (spike) data compression;
  • Point cloud compression;
  • Deep-learning-based quality enhancement of coding artefact removal;
  • Deep-learning-based depth estimation;
  • Deep-learning-based semantic segmentation;
  • Deep-learning-based instance segmentation;
  • Deep-learning-based image/video deblurring;
  • Learning-based image coding;
  • Image signal processing;
  • Event camera applications for computer vision;
  • 3D sensing and applications;
  • LiDAR;
  • ToF camera.

Dr. Ionut Schiopu
Dr. Radu Ciprian Bilcu
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

  • image and video coding
  • visual data compression
  • deep learning for image/video processing and computer vision
  • image signal processing
  • 3D sensing and applications

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Published Papers (1 paper)

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Research

21 pages, 2789 KiB  
Article
BIM-Based Adversarial Attacks Against Speech Deepfake Detectors
by Wendy Edda Wang, Davide Salvi, Viola Negroni, Daniele Ugo Leonzio, Paolo Bestagini and Stefano Tubaro
Electronics 2025, 14(15), 2967; https://doi.org/10.3390/electronics14152967 - 24 Jul 2025
Viewed by 324
Abstract
Automatic Speaker Verification (ASV) systems are increasingly employed to secure access to services and facilities. However, recent advances in speech deepfake generation pose serious threats to their reliability. Modern speech synthesis models can convincingly imitate a target speaker’s voice and generate realistic synthetic [...] Read more.
Automatic Speaker Verification (ASV) systems are increasingly employed to secure access to services and facilities. However, recent advances in speech deepfake generation pose serious threats to their reliability. Modern speech synthesis models can convincingly imitate a target speaker’s voice and generate realistic synthetic audio, potentially enabling unauthorized access through ASV systems. To counter these threats, forensic detectors have been developed to distinguish between real and fake speech. Although these models achieve strong performance, their deep learning nature makes them susceptible to adversarial attacks, i.e., carefully crafted, imperceptible perturbations in the audio signal that make the model unable to classify correctly. In this paper, we explore adversarial attacks targeting speech deepfake detectors. Specifically, we analyze the effectiveness of Basic Iterative Method (BIM) attacks applied in both time and frequency domains under white- and black-box conditions. Additionally, we propose an ensemble-based attack strategy designed to simultaneously target multiple detection models. This approach generates adversarial examples with balanced effectiveness across the ensemble, enhancing transferability to unseen models. Our experimental results show that, although crafting universally transferable attacks remains challenging, it is possible to fool state-of-the-art detectors using minimal, imperceptible perturbations, highlighting the need for more robust defenses in speech deepfake detection. Full article
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