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Electronics
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Recent Advances and Future Perspectives of Computer Vision and Image...
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Recent Advances and Future Perspectives of Computer Vision and Image Processing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 6224

Special Issue Editors

Prof. Dr. Bo Wang

E-Mail Website
Guest Editor
School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China
Interests: information processing for multimedia; multimedia security; artificial intelligence security
Dr. Wei Wang

E-Mail Website
Co-Guest Editor
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Interests: multimedia security and privacy; AI safety
Dr. Tong Qiao

E-Mail Website
Co-Guest Editor
School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310005, China
Interests: AI security; multimedia forensics; data hiding
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Visual media (e.g., image and video) is one of the most important information media. Computer vision (CV), as one of the hottest fields in artificial intelligence (AI), enables computers or other smart devices to derive information from visual contents, e.g., face recognition, auto-driving, medical image processing, etc. Image processing is a core component of CV, which is widely used to pre-process (e.g., achieving higher signal-to-noise ratio, image enhancing, etc.) and extract features from visual inputs. However, there are a number of problems yet to be solved in the literature; some methods which solve specific computer vision tasks cannot be directly applied in other situations.

This Special Issue aims to seek high-quality researches focusing on computer vision and image processing, and to bring together researchers from both academia and industry to provide their innovative insights into this Special Issue, titled Recent Advances and Future Perspectives of Computer Vision and Image Processing. We look forward to the latest research results that suggest theoretical and practical solutions for various application related to computer vision.

The topics of interest include, but are not limited to, the following:

  • computer vision;
  • image/video processing;
  • image/video content analysis;
  • image/video modeling;
  • multimedia security and forensics;
  • multimedia communications and networking;
  • multimedia systems and emerging applications.

Prof. Dr. Bo Wang
Dr. Wei Wang
Dr. Tong Qiao
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

  • computer vision
  • image/video processing
  • image/video content analysis
  • image/video modeling
  • multimedia security and forensics
  • multimedia communications and networking
  • multimedia systems and emerging applications

Published Papers (3 papers)

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Research

12 pages, 3534 KiB  
Article
CWAN: Covert Watermarking Attack Network
by Chunpeng Wang, Yushuo Liu, Zhiqiu Xia, Qi Li, Jian Li, Xiaoyu Wang and Bin Ma
Electronics 2023, 12(2), 303; https://doi.org/10.3390/electronics12020303 - 6 Jan 2023
Cited by 3 | Viewed by 1502
Abstract
Digital watermarking technology is widely used in today’s copyright protection, data monitoring, and data tracking. Digital watermarking attack techniques are designed to corrupt the watermark information contained in the watermarked image (WMI) so that the watermark information cannot be extracted effectively or correctly. [...] Read more.
Digital watermarking technology is widely used in today’s copyright protection, data monitoring, and data tracking. Digital watermarking attack techniques are designed to corrupt the watermark information contained in the watermarked image (WMI) so that the watermark information cannot be extracted effectively or correctly. While traditional digital watermarking attack technology is more mature, it is capable of attacking the watermark information embedded in the WMI. However, it is also more damaging to its own visual quality, which is detrimental to the protection of the original carrier and defeats the purpose of the covert attack on WMI. To advance watermarking attack technology, we propose a new covert watermarking attack network (CWAN) based on a convolutional neural network (CNN) for removing low-frequency watermark information from WMI and minimizing the damage caused by WMI through the use of deep learning. We import the preprocessed WMI into the CWAN, obtain the residual feature images (RFI), and subtract the RFI from the WMI to attack image watermarks. At this point, the WMI’s watermark information is effectively removed, allowing for an attack on the watermark information while retaining the highest degree of image detail and other features. The experimental results indicate that the attack method is capable of effectively removing the watermark information while retaining the original image’s texture and details and that its ability to attack the watermark information is superior to that of most traditional watermarking attack methods. Compared with the neural network watermarking attack methods, it has better performance, and the attack performance metrics are improved by tens to hundreds of percent in varying degrees, indicating that it is a new covert watermarking attack method. Full article
(This article belongs to the Special Issue Recent Advances and Future Perspectives of Computer Vision and Image Processing)
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19 pages, 10889 KiB  
Article
Research on High-Throughput Crop Root Phenotype 3D Reconstruction Using X-ray CT in 5G Era
by Jinpeng Wang, Haotian Liu, Qingxue Yao, Jeremy Gillbanks and Xin Zhao
Electronics 2023, 12(2), 276; https://doi.org/10.3390/electronics12020276 - 5 Jan 2023
Cited by 1 | Viewed by 1790
Abstract
Currently, the three-dimensional detection of plant root structure is one of the core issues in studies on plant root phenotype. Manual measurement methods are not only cumbersome but also have poor reliability and damage the root. Among many solutions, X-ray computed tomography (X-ray [...] Read more.
Currently, the three-dimensional detection of plant root structure is one of the core issues in studies on plant root phenotype. Manual measurement methods are not only cumbersome but also have poor reliability and damage the root. Among many solutions, X-ray computed tomography (X-ray CT) can help us observe the plant root structure in a three-dimensional and non-destructive form under the condition of underground soil in situ. Therefore, this paper proposes a high-throughput method and process for plant three-dimensional root phenotype and reconstruction based on X-ray CT technology. Firstly, this paper proposes a high-throughput transmission for the root phenotyping and utilizing the imaging technique to extract the root characteristics; then, the study adopts a moving cube algorithm to reconstruct the 3D (three-dimensional) root. Finally, this research simulates the proposed algorithm, and the simulation results show that the presented method in this paper works well. Full article
(This article belongs to the Special Issue Recent Advances and Future Perspectives of Computer Vision and Image Processing)
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12 pages, 5567 KiB  
Article
A Transformer-Based DeepFake-Detection Method for Facial Organs
by Ziyu Xue, Qingtong Liu, Haichao Shi, Ruoyu Zou and Xiuhua Jiang
Electronics 2022, 11(24), 4143; https://doi.org/10.3390/electronics11244143 - 12 Dec 2022
Cited by 2 | Viewed by 2230
Abstract
Nowadays, deepfake detection on subtle-expression manipulation, facial-detail modification, and smeared images has become a research hotspot. Existing deepfake-detection methods on the whole face are coarse-grained, where the details are missing due to the negligible manipulated size of the image. To address the problems, [...] Read more.
Nowadays, deepfake detection on subtle-expression manipulation, facial-detail modification, and smeared images has become a research hotspot. Existing deepfake-detection methods on the whole face are coarse-grained, where the details are missing due to the negligible manipulated size of the image. To address the problems, we propose to build a transformer model for a deepfake-detection method by organ, to obtain the deepfake features. We reduce the detection weight of defaced or unclear organs to prioritize the detection of clear and intact organs. Meanwhile, to simulate the real-world environment, we build a Facial Organ Forgery Detection Test Dataset (FOFDTD), which includes the images of mask face, sunglasses face, and undecorated face collected from the network. Experimental results on four benchmarks, i.e., FF++, DFD, DFDC-P, Celeb-DF, and for FOFDTD datasets, demonstrated the effectiveness of our proposed method. Full article
(This article belongs to the Special Issue Recent Advances and Future Perspectives of Computer Vision and Image Processing)
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