Usage of Deep Learning and Blockchain in Compilation and Copyright Protection of Digital Music

Cai, Zhini

doi:10.1109/access.2020.3021523

Cited by 27 publications

(7 citation statements)

References 35 publications

(39 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, students' learning motivation improved, and their performance of music style was roughly understood. Cai [ 9 ] explored the application of the DL algorithm in music arrangement and composition and the role of blockchain in digital music copyright protection. Firstly, a single tone melody composition model based on deep Generative Adversarial Networks (GANs) was constructed, and the synthesis performance of the model was analyzed with the hymn as the input sample.…”

Section: Introductionmentioning

confidence: 99%

Application of Lightweight Deep Learning Model in Vocal Music Education in Higher Institutions

Zhang

Liu

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The aim is to improve the teaching quality of music majors and cultivate their innovative ability. This article takes Vocal Music Education (VME) method as the research object to explore the teaching reform of Music Major courses. Firstly, this article makes an in-depth study on Big Data Analytics (BDA) and Digital Twins (DTs) technology and constructs a DTs platform connecting real teaching space and virtual teaching space. Secondly, the DTs platform is divided into online learning feature analysis and virtual-real teaching space integration functional modules. This article explores the online immersive education process design and technology application of the DTs platform from the two aspects of teaching and technology. Afterward, it designs a student action and expression recognition network based on the Visual Geometry Group (VGG) Net model and Google Net model in teaching data collection and management. Finally, the proposed system is tested. The test results show that the active and passive interaction curves of the traditional VME system have no obvious fluctuation, indicating that the interaction of the traditional VME system is not strong, and the ability of active feedback information is poor. By contrast, the active and passive interaction curves in the proposed VME have large fluctuations, showing that the proposed VME has more frequent interaction, and the teaching information can get real-time and active feedback. Therefore, the proposed VME system can better stimulate students’ learning desire. Meanwhile, the constructed Neural Network (NN) has the highest recognition accuracy of 99.07% on the student action and expression dataset. When tested with the image data taken by the research experiment, the highest accuracy is 89%, with an average of more than 85%. The proposed VME system provides ideas for applying DTs technology in the college of music education.

show abstract

Section: Introductionmentioning

confidence: 99%

Application of Lightweight Deep Learning Model in Vocal Music Education in Higher Institutions

Zhang

Liu

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…In 2020, Cai proposed a practical byzantine fault tolerance (IPBFT) algorithm. And a digital music copyright protection system is designed based on the blockchain [34]. Then in 2021, Liu et al proposed a post-quantum secure ring signature to enhance security and privacy in Cybertwin-driven 6G.…”

Section: B Privacy Protectionmentioning

confidence: 99%

“…At present, the latest Monroe coin scheme uses Bulletproofs technology to replace Borromean ring signatures and optimize the performance of the range proof algorithm. Related improved algorithms have also been proposed, such as Aurora [34], Libra [35], Ligero++ [36], Wolverine [37]. The introduction of methods such as zero knowledge proof and ring signature can technically achieve anonymization of blockchain nodes.…”

Section: B Privacy Protectionmentioning

confidence: 99%

Enhancing Copyright Protection Through Blockchain and Ring Signature Algorithm From Lattice

Jiang,

Gao,

2024

IEEE Access

View full text Add to dashboard Cite

Applying blockchain to copyright protection is currently a popular research trend. However, the characteristics of blockchain open data also lead to the threat of copyright privacy leakage. Targeting at this issue, we design a secure digital copyright protection scheme based on blockchain and ring signature algorithm, which can achieve privacy protection of copyright information and improve the efficiency of data authentication. In this paper, we design a new ring signature scheme based on lattice. In this scheme, by using the lattice basis delegation algorithm, user's public-private key pair is generated without expanding the dimension of lattice. Subsequently, according to the rejection sampling, message is signed by signer's secret keys and other ring participants' public keys. It can reduce scheme's computational complexity. Finally, by combining ring signatures with blockchain technology, a new copyright protection scheme is proposed. More importantly, this scheme is proven to be secure with its correctness and anonymity. Meanwhile, it has less communication costs and shorter key sizes than those in other similar schemes. The results show that the proposed new scheme has good performance and efficiency.

show abstract

“…Then, these hashes can be loaded into a blockchain, which can be managed by specific technologies and protocols to achieve complete protection. Cai et al [28] combined deep learning with blockchain to establish a digital system for safeguarding music copyrights and evaluated its performance.…”

Section: Blockchain Copyrightmentioning

confidence: 99%

Blockchain-based PDF File Copyright Protection and Tracing

Gao,

Wan,

Guo

et al. 2023

Preprint

View full text Add to dashboard Cite

As network technology advances at a rapid pace, an increasing number of individuals are utilizing the Internet to transmit and receive information. However, copying, altering, and other malicious acts constantly threaten the copyright ownership of electronic documents in this open environment of the Internet. Numerous copyright theft, illegal transmission, information forgery, and other problems are emerging. Moreover, because of the absence of copyright protection methods, PDF file owners often need help to secure the subsequent legal use of their PDF files after uploading them to the Internet. Combining blockchain and data hiding of PDF files for the first time, we propose a blockchain-based copyright protection scheme for PDF files to uniformly handle the problems of copyright authentication, authenticity identification, and copyright disputes of PDF files. First, we propose a new information hiding algorithm based on the incremental update characteristics of PDF, which embeds copyright information in PDF files. Second, the scheme enables users to establish their access control policies via smart contracts and delivers on-chain PDF file proofs, which can be employed to validate the ownership rights of PDF files and track the transaction history of PDF files. We have implemented the system on the Ethereum platform and analyzed its security and traceability. The experimental results demonstrate that the proposed information hiding algorithm possesses robust security and that the copyright registration method is rapid

show abstract

Usage of Deep Learning and Blockchain in Compilation and Copyright Protection of Digital Music

Cited by 27 publications

References 35 publications

Application of Lightweight Deep Learning Model in Vocal Music Education in Higher Institutions

Application of Lightweight Deep Learning Model in Vocal Music Education in Higher Institutions

Enhancing Copyright Protection Through Blockchain and Ring Signature Algorithm From Lattice

Blockchain-based PDF File Copyright Protection and Tracing

Contact Info

Product

Resources

About