Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.0
2.3
Journals
Mathematics
Special Issues
Advanced Mathematical Methods in Intelligent Multimedia: Security and Applications
share announcement

Advanced Mathematical Methods in Intelligent Multimedia: Security and Applications

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

Deadline for manuscript submissions: closed (20 March 2023) | Viewed by 30719

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Ximeng Liu

E-Mail Website
Guest Editor
1. College of Computer and Data Science, Fuzhou 350000, China
2. College of Software, Fuzhou University, Fuzhou 350000, China
Interests: applied cryptography; cloud security; big data security; privacy-preserving data mining/machine learning techniques; network security
Special Issues, Collections and Topics in MDPI journals
Dr. Yinbin Miao

E-Mail Website
Guest Editor
School of Cyber Engineering, Xidian University, Xi'an 710071, China
Interests: applied cryptography; data mining; blockchain
Special Issues, Collections and Topics in MDPI journals
Dr. Zuobin Ying

E-Mail Website
Guest Editor
Faculty of Data Science, City University of Macau, Macau
Interests: data privacy; blockchain; privacy-preserving machine-learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The area of intelligent multimedia involves the real-time computer processing and understanding of perceptual input from speech, textual and visual sources. It contrasts with the traditional display of text, voice, sound, and video/graphics with possibly touch and virtual reality linked in. The benefits of intelligent multimedia include improved productivity and efficiency, better flexibility and agility, and increased profitability. It also contains many applications that can improve automation, machine-to-machine communication, manufacturing oversite, and decision making. However, despite the advantage of intelligent multimedia, it also brings many security and privacy issues such as information confidentiality, data security, and secure communication. Most of the security and privacy issues can be solved with some mathematical cryptology methods. However, the heavyweight cryptosystem still cannot be performed on various types of multimedia, which restricts the applications in intelligent multimedia applications. This Special Issue is interested in inviting and gathering recent advanced mathematical methods in intelligent multimedia computing in security and applications to address these arising challenges and opportunities differently from traditional cloud-based architectures.

Prof. Dr. Ximeng Liu
Dr. Yinbin Miao
Dr. Zuobin Ying
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. Mathematics 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 2600 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

  • privacy computation
  • artificial intelligence
  • data mining and knowledge discovery
  • trust and reputation
  • formal security model
  • modelling and simulation
  • performance analysis and forecasting
  • optimization and operational research

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (13 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

29 pages, 5529 KiB  
Article
XTS: A Hybrid Framework to Detect DNS-Over-HTTPS Tunnels Based on XGBoost and Cooperative Game Theory
by Mungwarakarama Irénée, Yichuan Wang, Xinhong Hei, Xin Song, Jean Claude Turiho and Enan Muhire Nyesheja
Mathematics 2023, 11(10), 2372; https://doi.org/10.3390/math11102372 - 19 May 2023
Cited by 4 | Viewed by 1965
Abstract
This paper proposes a hybrid approach called XTS that uses a combination of techniques to analyze highly imbalanced data with minimum features. XTS combines cost-sensitive XGBoost, a game theory-based model explainer called TreeSHAP, and a newly developed algorithm known as Sequential Forward Evaluation [...] Read more.
This paper proposes a hybrid approach called XTS that uses a combination of techniques to analyze highly imbalanced data with minimum features. XTS combines cost-sensitive XGBoost, a game theory-based model explainer called TreeSHAP, and a newly developed algorithm known as Sequential Forward Evaluation algorithm (SFE). The general aim of XTS is to reduce the number of features required to learn a particular dataset. It assumes that low-dimensional representation of data can improve computational efficiency and model interpretability whilst retaining a strong prediction performance. The efficiency of XTS was tested on a public dataset, and the results showed that by reducing the number of features from 33 to less than five, the proposed model achieved over 99.9% prediction efficiency. XTS was also found to outperform other benchmarked models and existing proof-of-concept solutions in the literature. The dataset contained data related to DNS-over-HTTPS (DoH) tunnels. The top predictors for DoH classification and characterization were identified using interactive SHAP plots, which included destination IP, packet length mode, and source IP. XTS offered a promising approach to improve the efficiency of the detection and analysis of DoH tunnels while maintaining accuracy, which can have important implications for behavioral network intrusion detection systems. Full article
(This article belongs to the Special Issue Advanced Mathematical Methods in Intelligent Multimedia: Security and Applications)
►▼ Show Figures

Figure 1

20 pages, 637 KiB  
Article
Cloud-Assisted Private Set Intersection via Multi-Key Fully Homomorphic Encryption
by Cunqun Fan, Peiheng Jia, Manyun Lin, Lan Wei, Peng Guo, Xiangang Zhao and Ximeng Liu
Mathematics 2023, 11(8), 1784; https://doi.org/10.3390/math11081784 - 8 Apr 2023
Cited by 4 | Viewed by 2375
Abstract
With the development of cloud computing and big data, secure multi-party computation, which can collaborate with multiple parties to deal with a large number of transactions, plays an important role in protecting privacy. Private set intersection (PSI), a form of multi-party secure computation, [...] Read more.
With the development of cloud computing and big data, secure multi-party computation, which can collaborate with multiple parties to deal with a large number of transactions, plays an important role in protecting privacy. Private set intersection (PSI), a form of multi-party secure computation, is a formidable cryptographic technique that allows the sender and the receiver to calculate their intersection and not reveal any more information. As the data volume increases and more application scenarios emerge, PSI with multiple participants is increasingly needed. Homomorphic encryption is an encryption algorithm designed to perform a mathematical-style operation on encrypted data, where the decryption result of the operation is the same as the result calculated using unencrypted data. In this paper, we present a cloud-assisted multi-key PSI (CMPSI) system that uses fully homomorphic encryption over the torus (TFHE) encryption scheme to encrypt the data of the participants and that uses a cloud server to assist the computation. Specifically, we design some TFHE-based secure computation protocols and build a single cloud server-based private set intersection system that can support multiple users. Moreover, security analysis and performance evaluation show that our system is feasible. The scheme has a smaller communication overhead compared to existing schemes. Full article
(This article belongs to the Special Issue Advanced Mathematical Methods in Intelligent Multimedia: Security and Applications)
►▼ Show Figures

Figure 1

17 pages, 608 KiB  
Article
Privacy-Preserving Public Route Planning Based on Passenger Capacity
by Xin Zhang, Hua Zhang, Kaixuan Li and Qiaoyan Wen
Mathematics 2023, 11(6), 1546; https://doi.org/10.3390/math11061546 - 22 Mar 2023
Viewed by 1243
Abstract
Precise route planning needs huge amounts of trajectory data recorded in multimedia devices. The data, including each user’s location privacy, are stored as cipher text. The ability to plan routes on an encrypted trajectory database is an urgent necessity. In this paper, in [...] Read more.
Precise route planning needs huge amounts of trajectory data recorded in multimedia devices. The data, including each user’s location privacy, are stored as cipher text. The ability to plan routes on an encrypted trajectory database is an urgent necessity. In this paper, in order to plan a public route while protecting privacy, we design a hybrid encrypted random bloom filter (RBF) tree on encrypted databases, named the encrypted random bloom filter (eRBF) tree, which supports pruning and a secure, fast k nearest neighbor search. Based on the encrypted random bloom filter tree and secure computation of distance, we first propose a reverse k nearest neighbor trajectory search on encrypted databases (RkNNToE). It returns all transitions, in which each takes the query trajectory as one of its k nearest neighbor trajectories on the encrypted database. The results can be the indicator of a new route’s capacity in route planning. The security of the trajectory and query is proven via the simulation proof technique. When the number of points in the trajectory database and transition database are 1174 and 18,670, respectively, the time cost of an R2NNToE is about 1200 s. Full article
(This article belongs to the Special Issue Advanced Mathematical Methods in Intelligent Multimedia: Security and Applications)
►▼ Show Figures

Figure 1

18 pages, 1874 KiB  
Article
DDSG-GAN: Generative Adversarial Network with Dual Discriminators and Single Generator for Black-Box Attacks
by Fangwei Wang, Zerou Ma, Xiaohan Zhang, Qingru Li and Changguang Wang
Mathematics 2023, 11(4), 1016; https://doi.org/10.3390/math11041016 - 16 Feb 2023
Cited by 5 | Viewed by 2279
Abstract
As one of the top ten security threats faced by artificial intelligence, the adversarial attack has caused scholars to think deeply from theory to practice. However, in the black-box attack scenario, how to raise the visual quality of an adversarial example (AE) and [...] Read more.
As one of the top ten security threats faced by artificial intelligence, the adversarial attack has caused scholars to think deeply from theory to practice. However, in the black-box attack scenario, how to raise the visual quality of an adversarial example (AE) and perform a more efficient query should be further explored. This study aims to use the architecture of GAN combined with the model-stealing attack to train surrogate models and generate high-quality AE. This study proposes an image AE generation method based on the generative adversarial networks with dual discriminators and a single generator (DDSG-GAN) and designs the corresponding loss function for each model. The generator can generate adversarial perturbation, and two discriminators constrain the perturbation, respectively, to ensure the visual quality and attack effect of the generated AE. We extensively experiment on MNIST, CIFAR10, and Tiny-ImageNet datasets. The experimental results illustrate that our method can effectively use query feedback to generate an AE, which significantly reduces the number of queries on the target model and can implement effective attacks. Full article
(This article belongs to the Special Issue Advanced Mathematical Methods in Intelligent Multimedia: Security and Applications)
►▼ Show Figures

Figure 1

23 pages, 4142 KiB  
Article
DVIT—A Decentralized Virtual Items Trading Forum with Reputation System
by Zuobin Ying, Wusong Lan, Chen Deng, Lu Liu and Ximeng Liu
Mathematics 2023, 11(2), 429; https://doi.org/10.3390/math11020429 - 13 Jan 2023
Cited by 1 | Viewed by 2558
Abstract
The metaverse provides us with an attractive virtual space in which the value of the virtual property has been increasingly recognized. However, the lack of effective cross-metaverse trading tools and the reputation guarantee makes it difficult to trade items among different metaverses. To [...] Read more.
The metaverse provides us with an attractive virtual space in which the value of the virtual property has been increasingly recognized. However, the lack of effective cross-metaverse trading tools and the reputation guarantee makes it difficult to trade items among different metaverses. To this end, a decentralized reputation system for virtual items trading forum named DVIT is devised. To the best of our knowledge, DVIT is the first decentralized cross-metaverse item trading prototype inspired by the online-game trading system. We designed the corresponding transaction function and realized the autonomous governance of the community by introducing the reputation mechanism. An improved election mechanism is proposed to improve efficiency based on Delegated Proof-of-Stake (DPoS). Through token rewards associated with activity levels, users’ motivation can be stimulated. The experiments indicate that our proposed scheme could dynamically measure the trustworthiness degree of the users through the dynamic reputation value and thereby exclude malicious users from the blockchain within 20 epochs. Full article
(This article belongs to the Special Issue Advanced Mathematical Methods in Intelligent Multimedia: Security and Applications)
►▼ Show Figures

Figure 1

15 pages, 8756 KiB  
Article
A Differential Privacy Budget Allocation Algorithm Based on Out-of-Bag Estimation in Random Forest
by Xin Li, Baodong Qin, Yiyuan Luo and Dong Zheng
Mathematics 2022, 10(22), 4338; https://doi.org/10.3390/math10224338 - 18 Nov 2022
Cited by 5 | Viewed by 2738
Abstract
The issue of how to improve the usability of data publishing under differential privacy has become one of the top questions in the field of machine learning privacy protection, and the key to solving this problem is to allocate a reasonable privacy protection [...] Read more.
The issue of how to improve the usability of data publishing under differential privacy has become one of the top questions in the field of machine learning privacy protection, and the key to solving this problem is to allocate a reasonable privacy protection budget. To solve this problem, we design a privacy budget allocation algorithm based on out-of-bag estimation in random forest. The algorithm firstly calculates the decision tree weights and feature weights by the out-of-bag data under differential privacy protection. Secondly, statistical methods are introduced to classify features into best feature set, pruned feature set, and removable feature set. Then, pruning is performed using the pruned feature set to avoid decision trees over-fitting when constructing an ϵ-differential privacy random forest. Finally, the privacy budget is allocated proportionally based on the decision tree weights and feature weights in the random forest. We conducted experimental comparisons with real data sets from Adult and Mushroom to demonstrate that this algorithm not only protects data security and privacy, but also improves model classification accuracy and data availability. Full article
(This article belongs to the Special Issue Advanced Mathematical Methods in Intelligent Multimedia: Security and Applications)
►▼ Show Figures

Figure 1

15 pages, 1165 KiB  
Article
Efficient Reversible Data Hiding Based on Connected Component Construction and Prediction Error Adjustment
by Limengnan Zhou, Chongfu Zhang, Asad Malik and Hanzhou Wu
Mathematics 2022, 10(15), 2804; https://doi.org/10.3390/math10152804 - 7 Aug 2022
Cited by 3 | Viewed by 1676
Abstract
To achieve a good trade-off between the data-embedding payload and the data-embedding distortion, mainstream reversible data hiding (RDH) algorithms perform data embedding on a well-built prediction error histogram. This requires us to design a good predictor to determine the prediction errors of cover [...] Read more.
To achieve a good trade-off between the data-embedding payload and the data-embedding distortion, mainstream reversible data hiding (RDH) algorithms perform data embedding on a well-built prediction error histogram. This requires us to design a good predictor to determine the prediction errors of cover elements and find a good strategy to construct an ordered prediction error sequence to be embedded. However, many existing RDH algorithms use a fixed predictor throughout the prediction process, which does not take into account the statistical characteristics of local context. Moreover, during the construction of the prediction error sequence, these algorithms ignore the fact that adjacent cover elements may have the identical priority of data embedding. As a result, there is still room for improving the payload-distortion performance. Motivated by this insight, in this article, we propose a new content prediction and selection strategy for efficient RDH in digital images to provide better payload-distortion performance. The core idea is to construct multiple connected components for a given cover image so that the prediction errors of the cover pixels within a connected component are close to each other. Accordingly, the most suitable connected components can be preferentially used for data embedding. Moreover, the prediction errors of the cover pixels are adaptively adjusted according to their local context, allowing a relatively sharp prediction error histogram to be constructed. Experimental results validate that the proposed method is significantly superior to some advanced works regarding payload-distortion performance, demonstrating the practicality of our method. Full article
(This article belongs to the Special Issue Advanced Mathematical Methods in Intelligent Multimedia: Security and Applications)
►▼ Show Figures

Figure 1

19 pages, 2903 KiB  
Article
Toward Prevention of Parasite Chain Attack in IOTA Blockchain Networks by Using Evolutionary Game Model
by Yinfeng Chen, Yu Guo, Yaofei Wang and Rongfang Bie
Mathematics 2022, 10(7), 1108; https://doi.org/10.3390/math10071108 - 30 Mar 2022
Cited by 10 | Viewed by 2598
Abstract
IOTA is a new cryptocurrency system designed for the Internet of Things based on directed an acyclic graph structure. It has the advantages of supporting high concurrency, scalability, and zero transaction fees; however, due to the particularity of the directed acyclic graph structure, [...] Read more.
IOTA is a new cryptocurrency system designed for the Internet of Things based on directed an acyclic graph structure. It has the advantages of supporting high concurrency, scalability, and zero transaction fees; however, due to the particularity of the directed acyclic graph structure, IOTA faces more complex security threats than the sequence blockchain, in which a parasite chain attack is a common double-spending attack. In this work, we propose a scheme that can effectively prevent parasite chain attacks to improve the security of the IOTA ledger. Our main idea is to analyze the behavior strategies of IOTA nodes based on evolutionary game theory and determine the key factors affecting the parasite chain attack and the restrictive relationship between them. Based on the above research, we provide a solution to resist the parasite chain attack and further prove the effectiveness of the scheme by numerical simulation. Finally, we propose the parasite chain attack prevention algorithms based on price splitting to effectively prevent the formation of the parasite chain. Full article
(This article belongs to the Special Issue Advanced Mathematical Methods in Intelligent Multimedia: Security and Applications)
►▼ Show Figures

Figure 1

17 pages, 2063 KiB  
Article
A Blockchain-Empowered Arbitrable Multimedia Data Auditing Scheme in IoT Cloud Computing
by Shenling Wang, Yifang Zhang and Yu Guo
Mathematics 2022, 10(6), 1005; https://doi.org/10.3390/math10061005 - 21 Mar 2022
Cited by 14 | Viewed by 2546
Abstract
As increasing clients tend to outsource massive multimedia data generated by Internet of Things (IoT) devices to the cloud, data auditing is becoming crucial, as it enables clients to verify the integrity of their outsourcing data. However, most existing data auditing schemes cannot [...] Read more.
As increasing clients tend to outsource massive multimedia data generated by Internet of Things (IoT) devices to the cloud, data auditing is becoming crucial, as it enables clients to verify the integrity of their outsourcing data. However, most existing data auditing schemes cannot guarantee 100% data integrity and cannot meet the security requirement of practical multimedia services. Moreover, the lack of fair arbitration leads to clients not receiving compensation in a timely manner when the outsourced data is corrupted by the cloud service provider (CSP). In this work, we propose an arbitrable data auditing scheme based on the blockchain. In our scheme, clients usually only need to conduct private audits, and public auditing by a smart contract is triggered only when verification fails in private auditing. This hybrid auditing design enables clients to save audit fees and receive compensation automatically and in a timely manner when the outsourced data are corrupted by the CSP. In addition, by applying the deterministic checking technique based on a bilinear map accumulator, our scheme can guarantee 100% data integrity. Furthermore, our scheme can prevent fraudulent claims when clients apply for compensation from the CSP. We analyze the security strengths and complete the prototype’s implementation. The experimental results show that our blockchain-based data auditing scheme is secure, efficient, and practical. Full article
(This article belongs to the Special Issue Advanced Mathematical Methods in Intelligent Multimedia: Security and Applications)
►▼ Show Figures

Figure 1

19 pages, 2449 KiB  
Article
A k,n-Threshold Secret Image Sharing Scheme Based on a Non-Full Rank Linear Model
by Ji-Hwei Horng, Si-Sheng Chen and Chin-Chen Chang
Mathematics 2022, 10(3), 524; https://doi.org/10.3390/math10030524 - 7 Feb 2022
Cited by 3 | Viewed by 1963
Abstract
Secret image sharing is a hot issue in the research field of data hiding schemes for digital images. This paper proposes a general k,n threshold secret image sharing scheme, which distributes secret data into n meaningful image shadows based on a [...] Read more.
Secret image sharing is a hot issue in the research field of data hiding schemes for digital images. This paper proposes a general k,n threshold secret image sharing scheme, which distributes secret data into n meaningful image shadows based on a non-full rank linear model. The image shadows are indistinguishable from their corresponding distinct cover images. Any k combination of the n shares can perfectly restore the secret data. In the proposed scheme, the integer parameters k,n, with k≤n, can be set arbitrarily to meet the application requirement. The experimental results demonstrate the applicability of the proposed general scheme. The embedding capacity, the visual quality of image shadows, and the security level are satisfactory. Full article
(This article belongs to the Special Issue Advanced Mathematical Methods in Intelligent Multimedia: Security and Applications)
►▼ Show Figures

Figure 1

14 pages, 364 KiB  
Article
Stochastic Approximate Algorithms for Uncertain Constrained K-Means Problem
by Jianguang Lu, Juan Tang, Bin Xing and Xianghong Tang
Mathematics 2022, 10(1), 144; https://doi.org/10.3390/math10010144 - 4 Jan 2022
Viewed by 1547
Abstract
The k-means problem has been paid much attention for many applications. In this paper, we define the uncertain constrained k-means problem and propose a (1+ϵ)-approximate algorithm for the problem. First, a general mathematical model of the [...] Read more.
The k-means problem has been paid much attention for many applications. In this paper, we define the uncertain constrained k-means problem and propose a (1+ϵ)-approximate algorithm for the problem. First, a general mathematical model of the uncertain constrained k-means problem is proposed. Second, the random sampling properties of the uncertain constrained k-means problem are studied. This paper mainly studies the gap between the center of random sampling and the real center, which should be controlled within a given range with a large probability, so as to obtain the important sampling properties to solve this kind of problem. Finally, using mathematical induction, we assume that the first j−1 cluster centers are obtained, so we only need to solve the j-th center. The algorithm has the elapsed time O((1891ekϵ2)8k/ϵnd), and outputs a collection of size O((1891ekϵ2)8k/ϵn) of candidate sets including approximation centers. Full article
(This article belongs to the Special Issue Advanced Mathematical Methods in Intelligent Multimedia: Security and Applications)
►▼ Show Figures

Figure 1

16 pages, 2335 KiB  
Article
A Novel LSB Matching Algorithm Based on Information Pre-Processing
by Yongjin Hu, Xiyan Li and Jun Ma
Mathematics 2022, 10(1), 8; https://doi.org/10.3390/math10010008 - 21 Dec 2021
Cited by 3 | Viewed by 2440
Abstract
This paper analyzes random bits and scanned documents, two forms of secret data. The secret data were pre-processed by halftone, quadtree, and S-Box transformations, and the size of the scanned document was reduced by 8.11 times. A novel LSB matching algorithm with low [...] Read more.
This paper analyzes random bits and scanned documents, two forms of secret data. The secret data were pre-processed by halftone, quadtree, and S-Box transformations, and the size of the scanned document was reduced by 8.11 times. A novel LSB matching algorithm with low distortion was proposed for the embedding step. The golden ratio was firstly applied to find the optimal embedding position and was used to design the matching function. Both theory and experiment have demonstrated that our study presented a good trade-off between high capacity and low distortion and is superior to other related schemes. Full article
(This article belongs to the Special Issue Advanced Mathematical Methods in Intelligent Multimedia: Security and Applications)
►▼ Show Figures

Figure 1

20 pages, 4849 KiB  
Article
Reversible Data Hiding Based on Pixel-Value-Ordering and Prediction-Error Triplet Expansion
by Heng-Xiao Chi, Ji-Hwei Horng and Chin-Chen Chang
Mathematics 2021, 9(14), 1703; https://doi.org/10.3390/math9141703 - 20 Jul 2021
Cited by 4 | Viewed by 2501
Abstract
Pixel value ordering and prediction error expansion (PVO+PEE) is a very successful reversible data hiding (RDH) scheme. A series of studies were proposed to improve the performance of the PVO-based scheme. However, the embedding capacity of those schemes is quite limited. We propose [...] Read more.
Pixel value ordering and prediction error expansion (PVO+PEE) is a very successful reversible data hiding (RDH) scheme. A series of studies were proposed to improve the performance of the PVO-based scheme. However, the embedding capacity of those schemes is quite limited. We propose a two-step prediction-error-triplet expansion RDH scheme based on PVO. A three-dimensional state transition map for the prediction-error triplet is also proposed to guide the embedding of the two-step scheme. By properly designing the state transitions, the proposed scheme can embed secret data or expand without embedding by modifying just a single entry of the triplet. The experimental results show that the proposed scheme significantly enlarges the embedding capacity of the PVO-based scheme and further reduces the distortion due to embedding. Full article
(This article belongs to the Special Issue Advanced Mathematical Methods in Intelligent Multimedia: Security and Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-13
Back to TopTop