Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
Multimedia Smart Security
share announcement

Multimedia Smart Security

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 7208

Special Issue Editors

Dr. Jinwei Wang

E-Mail Website
Guest Editor
School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China
Interests: automotive engineering; automotive systems engineering; computer engineering
Dr. Chuan Qin

E-Mail Website
Guest Editor
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Interests: multimedia security; image processing; AI security
Special Issues, Collections and Topics in MDPI journals
Dr. Xiangyang Luo

E-Mail Website
Guest Editor
Institute of Information Engineering, Information Engineering University, Zhengzhou 450002, China
Interests: computer security and reliability

Special Issue Information

Dear Colleagues,

The topic of this Special Issue is “Multimedia Smart Security”. Multimedia (e.g., text, audio, image, and video) has been used in many fields, such as science, engineering, medicine, and business. Editing multimedia maliciously leads to the deception of human eyes or neural networks, which poses a huge challenge to multimedia security. Today, machine learning is performing well in various fields and has developed into a popular cross-discipline. Therefore, ensuring multimedia security through machine learning has become a research hotspot in the academic world.

We invite colleagues to submit research manuscripts on ”Multimedia Smart Security”.

Dr. Jinwei Wang
Dr. Chuan Qin
Dr. Xiangyang Luo
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. Applied Sciences 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

  • multimedia steganography
  • multimedia watermark
  • multimedia forensics
  • multimedia data encryption
  • multimedia data privacy protection
  • deepfake generation and defense
  • adversarial example

Published Papers (7 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

13 pages, 2817 KiB  
Article
Fast Fake: Easy-to-Train Face Swap Model
by Tomasz Walczyna and Zbigniew Piotrowski
Appl. Sci. 2024, 14(5), 2149; https://0-doi-org.brum.beds.ac.uk/10.3390/app14052149 - 04 Mar 2024
Viewed by 1125
Abstract
The proliferation of “Deep fake” technologies, particularly those facilitating face-swapping in images or videos, poses significant challenges and opportunities in digital media manipulation. Despite considerable advancements, existing methodologies often struggle with maintaining visual coherence, especially in preserving background features and ensuring the realistic [...] Read more.
The proliferation of “Deep fake” technologies, particularly those facilitating face-swapping in images or videos, poses significant challenges and opportunities in digital media manipulation. Despite considerable advancements, existing methodologies often struggle with maintaining visual coherence, especially in preserving background features and ensuring the realistic integration of identity traits. This study introduces a novel face replacement model that leverages a singular framework to address these issues, employing the Adaptive Attentional Denormalization mechanism from FaceShifter and integrating identity features via ArcFace and BiSeNet for enhanced attribute extraction. Key to our approach is the utilization of Fast GAN, optimizing the training efficiency of our model on relatively small datasets. We demonstrate the model’s efficacy in generating convincing face swaps with high fidelity, showcasing a significant improvement in blending identities seamlessly with the original background context. Our findings contribute to visual deepfake generation by enhancing realism and training efficiency but also highlight the potential for applications where authentic visual representation is crucial. Full article
(This article belongs to the Special Issue Multimedia Smart Security)
►▼ Show Figures

Figure 1

17 pages, 348 KiB  
Article
Covert Surveillance Video Transmission with QAM Constellations Modulation
by Sen Qiao, Guangjie Liu, Xiaopeng Ji, Junjie Zhao and Huihui Ding
Appl. Sci. 2024, 14(2), 577; https://0-doi-org.brum.beds.ac.uk/10.3390/app14020577 - 09 Jan 2024
Viewed by 506
Abstract
This study explores the covert transmission of surveillance videos using QAM (quadrature amplitude modulation) constellations. By analyzing transmission strategies used in surveillance video systems, we adopt KL divergence as the constraint for covertness performance and mutual information to characterize transmission rates. Utilizing the [...] Read more.
This study explores the covert transmission of surveillance videos using QAM (quadrature amplitude modulation) constellations. By analyzing transmission strategies used in surveillance video systems, we adopt KL divergence as the constraint for covertness performance and mutual information to characterize transmission rates. Utilizing the Taylor series expansion method, we simplify the complex integral calculations of mutual information and KL divergence into summation operations. This simplification not only reduces computational complexity but also ensures precise calculations. Theoretical derivations establish the necessary conditions for the covert transmission of surveillance videos, optimizing modulation probabilities for constellation points. Experimental results and simulation validations demonstrate the superior performance of our proposed method in terms of both information transfer and precision. Full article
(This article belongs to the Special Issue Multimedia Smart Security)
►▼ Show Figures

Figure 1

15 pages, 2760 KiB  
Article
Transmission Removal from a Single OSN-Shared Glass Mixture Image
by Heng Yao, Zhen Li and Chuan Qin
Appl. Sci. 2023, 13(23), 12779; https://0-doi-org.brum.beds.ac.uk/10.3390/app132312779 - 28 Nov 2023
Viewed by 490
Abstract
Photographs taken through glass often reflect the photographer or the surroundings, which is very helpful in uncovering information about the photograph. Various lossy operations performed on images over online social networks (OSNs), such as compression and resampling, pose a great challenge for transmission [...] Read more.
Photographs taken through glass often reflect the photographer or the surroundings, which is very helpful in uncovering information about the photograph. Various lossy operations performed on images over online social networks (OSNs), such as compression and resampling, pose a great challenge for transmission layer removal. This paper proposes a self-attention-based architecture for image enhancement over OSNs, to ensure that the downloaded glass mixture image can show more information about the reflection layer than the original image. Transmission layer removal is then achieved using a two-stage generative adversarial network. We also add attention to the transmission layer in the mixture image and use the gradient and color block information in the next stage to extract the reflection layer. This method yielded a gain of 0.46 dB in PSNR, 0.016 in SSIM, and 0.057 in LPIPS, resulting in an effective improvement in the visual quality of the final extracted reflection layer images. Full article
(This article belongs to the Special Issue Multimedia Smart Security)
►▼ Show Figures

Figure 1

20 pages, 37299 KiB  
Article
A Multi-Input Fusion Model for Privacy and Semantic Preservation in Facial Image Datasets
by Yuanzhe Yang, Zhiyi Niu, Yuying Qiu, Biao Song, Xinchang Zhang and Yuan Tian
Appl. Sci. 2023, 13(11), 6799; https://0-doi-org.brum.beds.ac.uk/10.3390/app13116799 - 02 Jun 2023
Viewed by 959
Abstract
The widespread application of multimedia technologies such as video surveillance, online meetings, and drones facilitates the acquisition of a large amount of data that may contain facial features, posing significant concerns with regard to privacy. Protecting privacy while preserving the semantic contents of [...] Read more.
The widespread application of multimedia technologies such as video surveillance, online meetings, and drones facilitates the acquisition of a large amount of data that may contain facial features, posing significant concerns with regard to privacy. Protecting privacy while preserving the semantic contents of facial images is a challenging but crucial problem. Contemporary techniques for protecting the privacy of images lack the incorporation of the semantic attributes of faces and disregard the protection of dataset privacy. In this paper, we propose the Facial Privacy and Semantic Preservation (FPSP) model that utilizes similar facial feature replacement to achieve identity concealment, while adding semantic evaluation to the loss function to preserve semantic features. The proposed model is versatile and efficient in different task scenarios, preserving image utility while concealing privacy. Our experiments on the CelebA dataset demonstrate that the model achieves a semantic preservation rate of 77% while concealing the identities in facial images in the dataset. Full article
(This article belongs to the Special Issue Multimedia Smart Security)
►▼ Show Figures

Figure 1

22 pages, 6483 KiB  
Article
Transmission Removal from a Single Glass Scene and Its Application in Photographer Identification
by Zhen Li, Heng Yao, Ran Shi, Tong Qiao and Chuan Qin
Appl. Sci. 2022, 12(23), 12484; https://0-doi-org.brum.beds.ac.uk/10.3390/app122312484 - 06 Dec 2022
Cited by 1 | Viewed by 979
Abstract
In daily life, when taking photos of scenes containing glass, the images of the dominant transmission layer and the weak reflection layer are often blended, which are difficult to be uncoupled. Meanwhile, because the reflection layer contains sufficient important information about the surrounding [...] Read more.
In daily life, when taking photos of scenes containing glass, the images of the dominant transmission layer and the weak reflection layer are often blended, which are difficult to be uncoupled. Meanwhile, because the reflection layer contains sufficient important information about the surrounding scene and the photographer, the problem of recovering the weak reflection layer from the mixture image is of importance in surveillance investigations. However, most of the current studies mainly focus on extracting the transmission layer while often ignoring the merit of the reflection layer. To fill that gap, in this paper, we propose a network framework that aims to accomplish two tasks: (1) for general scenes, we attempt to recover reflection layer images that are as close as possible to the ground truth ones, and (2) for scenes containing portraits, we recover the basic contour information of the reflection layer while improving the defects of dim portraits in the reflection layer. Through analyzing the performance exhibited by different levels of feature maps, we present the first transmission removal network based on an image-to-image translation architecture incorporating residual structures. The quality of generated reflection layer images is improved via tailored content and style constraints. We also use the patch generative adversarial network to increase the discriminator’s ability to perceive the reflection components in the generated images. Meanwhile, the related information such as edge and color distribution of transmission layer in the mixture image is used to assist the overall reflection layer recovery. In the large-scale experiments, our proposed model outperforms reflection removal-based SOTAs by more than 5.356 dB in PSNR, 0.116 in SSIM, and 0.057 in LPIPS. Full article
(This article belongs to the Special Issue Multimedia Smart Security)
►▼ Show Figures

Figure 1

21 pages, 4720 KiB  
Article
A Novel Localization Method of Wireless Covert Communication Entity for Post-Steganalysis
by Guo Wei, Shichang Ding, Haifeng Yang, Wenyan Liu, Meijuan Yin and Lingling Li
Appl. Sci. 2022, 12(23), 12224; https://0-doi-org.brum.beds.ac.uk/10.3390/app122312224 - 29 Nov 2022
Cited by 1 | Viewed by 935
Abstract
Recently, some criminals have begun to use multimedia steganography to conduct covert communication, such as transmitting stolen trade secrets. After using steganalysis to find covert communication entities, obtaining their locations can effectively help criminal forensics. This paper proposes a novel localization method of [...] Read more.
Recently, some criminals have begun to use multimedia steganography to conduct covert communication, such as transmitting stolen trade secrets. After using steganalysis to find covert communication entities, obtaining their locations can effectively help criminal forensics. This paper proposes a novel localization method of wireless covert communication entity for post-steganalysis. The method is based on hybrid particle swarm optimization and gray wolf optimization to improve localization precision (ILP-PSOGWO). In this method, firstly, the relationship model between received signal strength (RSS) and distance is constructed for the indoor environment where the target node exists. Secondly, dichotomy is used to narrow the region where the target node is located. Then, the weighted distance strategy is used to select the reference point locations with strong and stable RSS. Finally, the intersection region of the reference points is taken as the region where the target node is located, and the hybrid PSOGWO is used to locate and optimize the target node location. Experimental results demonstrate that ILP-PSOGWO can maintain high stability, and 90% of the localization errors are lower than 0.9012 m. In addition, compared with the existing methods of PSO, GWO and extended weighted centroid localization (EWCL), the average localization error of ILP-PSOGWO is also reduced by 28.2–49.0%. Full article
(This article belongs to the Special Issue Multimedia Smart Security)
►▼ Show Figures

Figure 1

11 pages, 1049 KiB  
Article
A New Imbalanced Encrypted Traffic Classification Model Based on CBAM and Re-Weighted Loss Function
by Jiayu Qin, Guangjie Liu and Kun Duan
Appl. Sci. 2022, 12(19), 9631; https://0-doi-org.brum.beds.ac.uk/10.3390/app12199631 - 25 Sep 2022
Viewed by 1459
Abstract
The accurate classification of traffic data is challenging for network management and security, especially in imbalanced situations. The limitation of the existing convolutional neural networks is that they have problems such as overfitting, instability, and poor generalization when used to classify imbalanced datasets. [...] Read more.
The accurate classification of traffic data is challenging for network management and security, especially in imbalanced situations. The limitation of the existing convolutional neural networks is that they have problems such as overfitting, instability, and poor generalization when used to classify imbalanced datasets. In this paper, we propose a new imbalanced encrypted traffic classification model. The proposed model is based on the improved convolutional block attention module (CBAM) and re-weighted cross-entropy focal loss (CEFL) function. The model exploits the redefined imbalance degree to construct a weight function, which is used to reassign the weights of the categories. The improved CBAM based on the redefined imbalance degree can make the model pay more attention to the characteristics of the minority samples, and increase the representation ability of these samples. The re-weighted CEFL loss function can be used to expand the effective loss gap between minority and majority samples. The method is validated on the public ISCX Tor 2016 dataset. The experimental results show that the performance of the new classification model is better than the baseline methods, and the proposed method can remarkably push the precision of the minority categories to 93.28% (14.63%↑), recall to 91.71% (16.98%↑), and F1 score to 92.49% (16.23%↑). Full article
(This article belongs to the Special Issue Multimedia Smart Security)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop