Computational Methods and Algorithms for Multimedia Data Analysis and Security

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

Deadline for manuscript submissions: 31 January 2025 | Viewed by 3538

Special Issue Editors


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Guest Editor
School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China
Interests: pattern recognition; computer vision; machine learning; image processing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Cyber Science and Engineering, Southeast University, Nanjing, China
Interests: cryptography and security protocols; internet of things security; big data security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the proliferation of multimedia data across various platforms has led to an urgent need for effective analysis algorithms and robust data security measures. Multimedia data, including images, videos, and audio files, present unique challenges due to their diverse formats, large volumes, and complex content. Furthermore, ensuring the confidentiality, integrity, and availability of multimedia data is crucial for protecting sensitive information and maintaining user privacy. This Special Issue aims to showcase cutting-edge research in the fields of multimedia computing, data security algorithms, and optimization strategies, highlighting innovative algorithms, methodologies, and solutions to address these challenges. Within this context, the topics covered in the Special Issue encompass diverse deep learning models incorporating blockchain or repurposing. These models span various applications, including transfer learning, meta-learning, continuous learning, model fine-tuning, model retraining, model reuse, representation learning, blockchain technology, federated learning, etc.

We look forward to receiving your interesting submissions.

Topics include, but are not limited to, the following:

  • Advanced multimedia content computing and understanding techniques;
  • Deep learning and artificial intelligence for multimedia data processing;
  • Multimedia feature extraction, representation, and classification;
  • Multimedia retrieval algorithm;
  • Privacy-preserving multimedia processing and sharing;
  • Multimedia authentication and integrity verification;
  • Secure multimedia communication and streaming protocols;
  • Multimedia data protection in cloud computing and edge computing environments;
  • Person re-identification;
  • Blockchain theory and application;
  • Blockchain regulation and technology.

Dr. Keyang Cheng
Prof. Dr. Liangmin Wang
Guest Editors

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Keywords

  • multimedia computing
  • transfer learning
  • meta learning
  • continuous learning
  • model fine-tuning
  • model retraining
  • model reuse
  • representation learning
  • blockchain
  • federated learning

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Published Papers (3 papers)

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Research

20 pages, 3578 KiB  
Article
TOAR: Toward Resisting AS-Level Adversary Correlation Attacks Optimal Anonymous Routing
by Hui Zhao and Xiangmei Song
Mathematics 2024, 12(23), 3640; https://doi.org/10.3390/math12233640 - 21 Nov 2024
Viewed by 470
Abstract
The Onion Router (Tor), as the most widely used anonymous network, is vulnerable to traffic correlation attacks by powerful passive adversaries, such as Autonomous Systems (AS). AS-level adversaries increase their chances of executing correlation attacks by manipulating the underlying routing, thereby compromising anonymity. [...] Read more.
The Onion Router (Tor), as the most widely used anonymous network, is vulnerable to traffic correlation attacks by powerful passive adversaries, such as Autonomous Systems (AS). AS-level adversaries increase their chances of executing correlation attacks by manipulating the underlying routing, thereby compromising anonymity. Furthermore, these underlying routing detours in the Tor client’s routing inference introduce extra latency. To address this challenge, we propose Toward Resisting AS-level Adversary Correlation Attacks Optimal Anonymous Routing (TOAR). TOAR is a two-stage routing mechanism based on Bayesian optimization within Software Defined Networks (SDN), comprising route search and route forwarding. Specifically, it searches for routes that conform to established policies, avoiding AS that could connect traffic between clients and destinations while maintaining anonymity in the selection of routes that minimize communication costs. To evaluate the anonymity of TOAR, as well as the effectiveness of route searching and the performance of route forwarding, we conduct a detailed analysis and extensive experiments. The analysis and experimental results show that the probability of routing being compromised by correlation attacks is significantly reduced. Compared to classical enumeration-based methods, the success rate of route searching increased by close to 2.5 times, and the forwarding throughput reached 70% of that of the packet transmission. The results show that TOAR effectively improves anonymity while maintaining communication quality, minimizing anonymity loss from AS-level adversaries and reducing high latency from routing detours. Full article
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13 pages, 1781 KiB  
Article
SEAIS: Secure and Efficient Agricultural Image Storage Combining Blockchain and Satellite Networks
by Haotian Yang, Pujie Jing, Zihan Wu, Lu Liu and Pengyan Liu
Mathematics 2024, 12(18), 2861; https://doi.org/10.3390/math12182861 - 14 Sep 2024
Viewed by 682
Abstract
The image integrity of real-time monitoring is crucial for monitoring crop growth, helping farmers and researchers improve production efficiency and crop yields. Unfortunately, existing schemes just focus on ground equipment and drone imaging, neglecting satellite networks in remote or extreme environments. Given that [...] Read more.
The image integrity of real-time monitoring is crucial for monitoring crop growth, helping farmers and researchers improve production efficiency and crop yields. Unfortunately, existing schemes just focus on ground equipment and drone imaging, neglecting satellite networks in remote or extreme environments. Given that satellite internet features wide area coverage, we propose SEAIS, a secure and efficient agricultural image storage scheme combining blockchain and satellite networks. SEAIS presents the mathematical model of image processing and transmission based on satellite networks. Moreover, to ensure the integrity and authenticity of image data during pre-processing such as denoising and enhancement, SEAIS includes a secure agricultural image storage and verification method based on blockchain, homomorphic encryption, and zero-knowledge proof. Specifically, images are stored via IPFS, with hash values and metadata recorded on the blockchain, ensuring immutability and transparency. The simulation results show that SEAIS exhibits more stable and efficient processing times in extreme environments. Also, it maintains low on-chain storage overhead, enhancing scalability. Full article
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23 pages, 4800 KiB  
Article
Blockchain Interoperability in Data Exchange Logistics Integration
by Kaiye Li, Chun Wang, Xia Feng and Songze Wu
Mathematics 2024, 12(10), 1516; https://doi.org/10.3390/math12101516 - 13 May 2024
Viewed by 1345
Abstract
Logistics companies are increasingly adopting private blockchains for enhanced data management because of the trends in cooperation. Nevertheless, this practice poses new challenges concerning the security and sharing of data. The real-time nature and diversity of logistics data increase the difficulty of protecting [...] Read more.
Logistics companies are increasingly adopting private blockchains for enhanced data management because of the trends in cooperation. Nevertheless, this practice poses new challenges concerning the security and sharing of data. The real-time nature and diversity of logistics data increase the difficulty of protecting the data. Additionally, when transportation information changes, downstream enterprises must promptly adjust their production plans to accommodate these alterations. The strict access controls of private blockchains can obstruct downstream enterprises from obtaining data, posing a challenge to the overall operational efficiency. In this paper, we propose an innovative logistics data protection scheme that employs private set intersection (PSI) and blockchain cross-chain technology to achieve data security. In our scheme, logistics companies within the logistics consortium are added as trusted agents to the public blockchain, enabling downstream enterprises to acquire logistics data integration from the public blockchain. Utilizing an RSA-based PSI protocol, our approach enhances exchange efficiency while protecting private data without transmitting additional information. We evaluate the performance of the proposed solution through a series of experiments, and the results demonstrate that our solution can achieve secure and efficient logistics data exchange. Full article
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