Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.9
Journals
Electronics
Special Issues
Digital and Optical Security Algorithms via Machine Learning
share announcement

Digital and Optical Security Algorithms via Machine Learning

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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 11205

Special Issue Editors

Dr. Shuming Jiao

E-Mail Website
Guest Editor
Peng Cheng Laboratory, No. 2 Xingke 1st Street, Shenzhen 518066, China
Interests: holographic three-dimensional imaging and display; single-pixel compressive imaging, optical information processing; optical computing; image processing; information security; machine learning
Dr. Hongbo Zhang

E-Mail Website
Guest Editor
Department of Engineering Technology, Middle Tennessee State University, 1301 E Main St., Murfreesboro, TN 37132, USA
Interests: digital holography; microscopy; X-ray laser
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is well known that information security is a very critical issue in modern society. Various encryption, watermarking and authentication techniques can prevent the illegal access of text, audio, image, video and other forms of information. In addition to digital security techniques, optical security systems provide an alternative solution with unique features. Machine learning algorithms including deep learning has received much attention among many fields in the past decade. Machine learning can be potentially applied in the design of intelligent digital or optical security systems. At the same time, the wide use of machine learning introduces new types of security issues such as adversarial attack.  Researchers are invited to submit articles about novel intelligent digital and optical security algorithms via machine learning to this special issue. Interdisciplinary research works related to security are especially encouraged to be submitted to this issue. The scope of this special issue includes but not limited to

  • Design of digital/optical encryption, watermarking and other security techniques via machine learning
  • Cryptanalysis of digital/optical security systems via machine learning
  • Forensics with deep learning
  • Encryption and information hiding in deep learning model
  • Privacy-preserved optical imaging
  • Adversarial attacks and defenses in deep learning and other machine learning techniques

Dr. Shuming Jiao
Dr. Hongbo Zhang
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

  • encryption
  • watermarking
  • hiding
  • authentication
  • machine learning
  • deep learning
  • security
  • optical
  • cryptanalysis
  • attack

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

9 pages, 3218 KiB  
Article
Broadband and Wide-Angle Performance of a Perfect Absorber Using a MIM Structure with 2D MXene
by Yue Jia, Chunmei Song, Yunlong Liao and Houzhi Cai
Electronics 2022, 11(9), 1370; https://doi.org/10.3390/electronics11091370 - 25 Apr 2022
Cited by 3 | Viewed by 1573
Abstract
Due to the extraordinary optoelectronic properties and surface-rich functional groups, MXene has shown great promise in many applications, such as electromagnetic shielding, catalysis, sensors, ultrafast photons, etc. In this work, we propose a wide-angle absorber based on a metal-insulator-metal (MIM) metamaterial consisting of [...] Read more.
Due to the extraordinary optoelectronic properties and surface-rich functional groups, MXene has shown great promise in many applications, such as electromagnetic shielding, catalysis, sensors, ultrafast photons, etc. In this work, we propose a wide-angle absorber based on a metal-insulator-metal (MIM) metamaterial consisting of MXene. By optimizing the design, the absorption efficiency can be further improved throughout the entire wavelength range. More importantly, the absorber exhibits high-efficiency broadband and wide-angle (20–80°) absorption in the near-infrared range (NIR: 1.1–1.7 μm) by numerical calculation. It is foreseeable that the excellent absorption characteristics and easy-to-manufacture structure of the designed absorber will bring some inspiration to the absorption device in the NIR and its practical application. Full article
(This article belongs to the Special Issue Digital and Optical Security Algorithms via Machine Learning)
►▼ Show Figures

Figure 1

15 pages, 4417 KiB  
Article
An End-to-End Video Steganography Network Based on a Coding Unit Mask
by Huanhuan Chai, Zhaohong Li, Fan Li and Zhenzhen Zhang
Electronics 2022, 11(7), 1142; https://doi.org/10.3390/electronics11071142 - 05 Apr 2022
Cited by 6 | Viewed by 2448
Abstract
Steganography hides secret messages inside the covers while ensuring imperceptibility. Different from traditional steganography, deep learning-based steganography has an adaptable and generalized framework without needing expertise regarding the embedding process. However, most steganography algorithms utilize images as covers instead of videos, which are [...] Read more.
Steganography hides secret messages inside the covers while ensuring imperceptibility. Different from traditional steganography, deep learning-based steganography has an adaptable and generalized framework without needing expertise regarding the embedding process. However, most steganography algorithms utilize images as covers instead of videos, which are more expressive and more widely spread. To this end, an end-to-end deep learning network for video steganography is proposed in this paper. A multiscale down-sampling feature extraction structure is designed, which consists of three parts including an encoder, a decoder, and a discriminator network. Furthermore, in order to facilitate the learning ability of network, a CU (coding unit) mask built from a VVC (versatile video coding) video is first introduced. In addition, an attention mechanism is used to further promote the visual quality. The experimental results show that the proposed steganography network can achieve a better performance in terms of the perceptual quality of stego videos, decoding the accuracy of hidden messages, and the relatively high embedding capacity compared with the state-of-the-art steganography networks. Full article
(This article belongs to the Special Issue Digital and Optical Security Algorithms via Machine Learning)
►▼ Show Figures

Figure 1

9 pages, 2428 KiB  
Article
Sensitivity Improvement of Surface Plasmon Resonance Biosensors with GeS-Metal Layers
by Yue Jia, Yunlong Liao and Houzhi Cai
Electronics 2022, 11(3), 332; https://doi.org/10.3390/electronics11030332 - 21 Jan 2022
Cited by 12 | Viewed by 2298
Abstract
Surface plasmon resonance (SPR) biosensors, with germanium sulfide (GeS) as a sensitive medium and Al/Ag/Au as the metal layers, are reported as we aim to improve the sensitivities of the biosensors. The sensitivities in conventional SPR biosensors, consisting of only metal Al, Ag, [...] Read more.
Surface plasmon resonance (SPR) biosensors, with germanium sulfide (GeS) as a sensitive medium and Al/Ag/Au as the metal layers, are reported as we aim to improve the sensitivities of the biosensors. The sensitivities in conventional SPR biosensors, consisting of only metal Al, Ag, and Au layers, are 111°/RIU, 117°/RIU, 139°/RIU, respectively. Additionally, these sensitivities of the SPR biosensors based on the GeS-Al, GeS-Ag, and GeS-Au layers have an obvious improvement, resultant of 320°/RIU, 295°/RIU, and 260°/RIU, respectively. We also discuss the changing sensing medium GeS thickness using layer number to describe the scenario which brought about the diversification on the figure of merit (FOM) and optical absorption (OA) performance of the biosensors. These biosensors show obvious improvement of sensitivity and have strong SPR excitation to analytes; we believe that these kind biosensors could find potential applications in biological detection, chemical examination, and medical diagnosis. Full article
(This article belongs to the Special Issue Digital and Optical Security Algorithms via Machine Learning)
►▼ Show Figures

Figure 1

13 pages, 2447 KiB  
Article
LPNet: Retina Inspired Neural Network for Object Detection and Recognition
by Jie Cao, Chun Bao, Qun Hao, Yang Cheng and Chenglin Chen
Electronics 2021, 10(22), 2883; https://doi.org/10.3390/electronics10222883 - 22 Nov 2021
Cited by 3 | Viewed by 2284
Abstract
The detection of rotated objects is a meaningful and challenging research work. Although the state-of-the-art deep learning models have feature invariance, especially convolutional neural networks (CNNs), their architectures did not specifically design for rotation invariance. They only slightly compensate for this feature through [...] Read more.
The detection of rotated objects is a meaningful and challenging research work. Although the state-of-the-art deep learning models have feature invariance, especially convolutional neural networks (CNNs), their architectures did not specifically design for rotation invariance. They only slightly compensate for this feature through pooling layers. In this study, we propose a novel network, named LPNet, to solve the problem of object rotation. LPNet improves the detection accuracy by combining retina-like log-polar transformation. Furthermore, LPNet is a plug-and-play architecture for object detection and recognition. It consists of two parts, which we name as encoder and decoder. An encoder extracts images which feature in log-polar coordinates while a decoder eliminates image noise in cartesian coordinates. Moreover, according to the movement of center points, LPNet has stable and sliding modes. LPNet takes the single-shot multibox detector (SSD) network as the baseline network and the visual geometry group (VGG16) as the feature extraction backbone network. The experiment results show that, compared with conventional SSD networks, the mean average precision (mAP) of LPNet increased by 3.4% for regular objects and by 17.6% for rotated objects. Full article
(This article belongs to the Special Issue Digital and Optical Security Algorithms via Machine Learning)
►▼ Show Figures

Figure 1

15 pages, 1397 KiB  
Article
A Novel Technique for Image Steganalysis Based on Separable Convolution and Adversarial Mechanism
by Yuwei Ge, Tao Zhang, Haihua Liang, Qingfeng Jiang and Dan Wang
Electronics 2021, 10(22), 2742; https://doi.org/10.3390/electronics10222742 - 10 Nov 2021
Cited by 3 | Viewed by 1676
Abstract
Image steganalysis is a technique for detecting the presence of hidden information in images, which has profound significance for maintaining cyberspace security. In recent years, various deep steganalysis networks have been proposed in academia, and have achieved good detection performance. Although convolutional neural [...] Read more.
Image steganalysis is a technique for detecting the presence of hidden information in images, which has profound significance for maintaining cyberspace security. In recent years, various deep steganalysis networks have been proposed in academia, and have achieved good detection performance. Although convolutional neural networks (CNNs) can effectively extract the features describing the image content, the difficulty lies in extracting the subtle features that describe the existence of hidden information. Considering this concern, this paper introduces separable convolution and adversarial mechanism, and proposes a new network structure that effectively solves the problem. The separable convolution maximizes the residual information by utilizing its channel correlation. The adversarial mechanism makes the generator extract more content features to mislead the discriminator, thus separating more steganographic features. We conducted experiments on BOSSBase1.01 and BOWS2 to detect various adaptive steganography algorithms. The experimental results demonstrate that our method extracts the steganographic features effectively. The separable convolution increases the signal-to-noise ratio, maximizes the channel correlation of residuals, and improves efficiency. The adversarial mechanism can separate more steganographic features, effectively improving the performance. Compared with the traditional steganalysis methods based on deep learning, our method shows obvious improvements in both detection performance and training efficiency. Full article
(This article belongs to the Special Issue Digital and Optical Security Algorithms via Machine Learning)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop