Recent Advances in Deep Learning Algorithms for Computer Vision and Image Analysis

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 737

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Guest Editor
Industrial and Systems Engineering Graduate Program—PPGEPS, Pontifical Catholic University of Parana—PUCPR, Curitiba, Brazil
Interests: computer vision; image processing; 3D reconstruction; realistic simulations

Special Issue Information

Dear Colleagues,

Computer vision has become a key area of modern technology, driving innovation in automation, intelligent systems, and human–machine interaction to address a wide range of real-world challenges. Powered by deep learning, it enables machines to interpret and understand visual information, opening new horizons for science, engineering, and society.

This Special Issue aims to bring together cutting-edge contributions at the intersection of engineering, computer science, and related disciplines, highlighting innovative methods, architectures, and applications. The topics of interest include, but are not limited to, the use of deep neural networks, convolutional architectures, transformer-based vision models, image segmentation, object detection, image enhancement, 3D reconstruction, multimodal data fusion, and explainable AI in vision systems. We particularly welcome work addressing multidisciplinary applications, such as robotics, industrial automation, biomedical engineering, smart cities, and intelligent transportation. We also encourage the submission of studies on how computer vision and image analysis are transforming the creation, validation, and operation of virtual models and realistic simulations in the context of digital twins across multiple domains.Submissions should present novel methodologies, theoretical advances, or significant practical implementations. Both experimental and theoretical studies are encouraged, as well as interdisciplinary research combining knowledge from various engineering fields.

Prof. Dr. Marcelo Rudek
Guest Editor

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Keywords

  • deep neural networks
  • convolutional architectures
  • transformer-based vision models
  • object detection and tracking
  • image segmentation
  • image enhancement
  • 3D reconstruction
  • multimodal data fusion
  • explainable AI in vision systems
  • vision in virtual simulations and digital twins

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Published Papers (1 paper)

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Research

19 pages, 1771 KB  
Article
Dynamic Spatial-Temporal Inconsistency Learning for General Deepfake Detection in Visual Understanding
by Jicheng Li, Guangjun Liao, Yufei Wang, Xing Liu and Beibei Liu
Mathematics 2026, 14(10), 1612; https://doi.org/10.3390/math14101612 - 9 May 2026
Viewed by 304
Abstract
Generalizable deepfake detection is essential for trustworthy visual understanding in real-world computer vision applications. This paper presents a dynamic spatial-temporal inconsistency learning algorithm designed to achieve high generalization in deepfake video detection. Current video-based detection approaches tend to either isolate spatial artifacts or [...] Read more.
Generalizable deepfake detection is essential for trustworthy visual understanding in real-world computer vision applications. This paper presents a dynamic spatial-temporal inconsistency learning algorithm designed to achieve high generalization in deepfake video detection. Current video-based detection approaches tend to either isolate spatial artifacts or merely exploit coarse temporal inconsistencies when identifying deepfake videos, which impedes the acquisition of fine-grained spatial-temporal clues and consequently limits their generalization capability. To this end, we propose the dynamic spatial-temporal network (DST-Net), a deep architecture that systematically mines comprehensive inconsistency cues through three synergistic modules. The short-term temporal modality extraction (STME) module captures temporal dynamics from adjacent frames. The short-term spatial-temporal inconsistency extraction (SSTIE) module with pixel-wise supervision learns semantically meaningful inconsistency features resistant to perturbations. The dynamic-term spatial-temporal inconsistency extraction (DSTIE) module adaptively aggregates these features across timescales, building robust multi-scale representations. This design ensures that the learned representations capture intrinsic forgery patterns, enhancing generalization and robustness. Comprehensive evaluations conducted on five widely adopted benchmark datasets reveal that our method surpasses nine representative competitors, with superior robustness to common image perturbations. This work advances the application of deep learning algorithms to reliable visual understanding in multimedia forensics. Full article
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