Follow blockchain research across journals, conferences, and preprint repositories.
15 results · page 1 of 1
Arman Daliri, Nora Mahdavi, Mahdieh Zabihimayvan, Aynaz Norouzi Baranghar · 6 authors
No abstract is available for this record.
Jianan Liu, Yongjuan Wang, Siqi Lu, Gang Yu · 6 authors
Abstract The rapid growth in the speed and convenience of information dissemination has made copyright infringement increasingly common. Blockchain technology solves pain points such as difficulties in traditional copyright registration, easy infringement, and difficulties in confirming and safeguarding rights. It also realises the decentralised management of copyright, network-wide tracking and monitoring, trusted certificate deposits, among others. However, the efficient original authentication of works and the function of blockchain to create copyright trading channels in the field of copyright are often ignored. This paper designed a self-adaptive learning similarity detection fusion strategy to protect the copyright of original digital works, namely SAAChain, and built a platform for releasing and storing original works based on non-fungible tokens. SAAChain first measures the similarity of a work based on adaptive learning to realise the originality authentication of works. Secondly, the works are stored on the InterPlanetary File System as NFTs, along with copyright information. Finally, a smart contract based on Ethereum and ERC-721 is designed to realise the free circulation of digital rights while simultaneously constructing an efficient and convenient digital rights protection system. Experiments show that the accuracy of the fusion strategy for adaptive work similarity detection can reach above 97%, which meets the requirements of work originality verification. Because of the storage mode of the platform, the system has good performance in terms of response speed and storage efficiency. The entire process provides a full-process and transparent transaction platform for all parties and guarantees the copyright ownership of works as well as the non-tampering and traceability of copyright information.
Zening Zhao, Jinsong Wang, Miao Yang, Haitao Wang
The Bitcoin network comprises numerous nodes, necessitating users to invest significant network requests and time in comprehending its network topology. In this paper, we propose a Bitcoin network topology discovery algorithm that utilizes lightweight probe nodes to facilitate rapid transmission of network protocols. Building upon this, we introduce a node layer clustering algorithm based on filtering stable network nodes, enabling parallel discovery of the network topology. Additionally, we present an adaptive method for dynamically displaying the layered structure of the network topology. Experimental results demonstrate that our proposed method reduces communication overhead by approximately 72.16% when achieving a 95% similarity in network topology. Furthermore, the algorithm is applicable for discovering the network topology in other blockchain networks with similar structures.
Xiaoqing Wen, Quanbi Feng, Hanzheng Lyu, Jianyu Niu · 6 authors
Rollups have emerged as a promising approach to improving blockchains' scalability by offloading transactions execution off-chain. Existing rollup solutions either leverage complex zero-knowledge proofs or optimistically assume execution correctness unless challenged. However, these solutions suffer from high gas costs and significant withdrawal delays, hindering their adoption in decentralized applications. This paper introduces TEERollup, an efficient rollup protocol that leverages Trusted Execution Environments (TEEs) to achieve both low gas costs and short withdrawal delays. Sequencers (system participants) execute transactions within TEEs and upload signed execution results to the blockchain with confidential keys of TEEs. Unlike most TEE-assisted blockchain designs, TEERollup adopts a practical threat model where the integrity and availability of TEEs may be compromised. To address these issues, we first introduce a distributed system of sequencers with heterogeneous TEEs, ensuring system security even if a certain proportion of TEEs are compromised. Second, we propose a challenge mechanism to solve the redeemability issue caused by TEE unavailability. Furthermore, TEERollup incorporates Data Availability Providers (DAPs) to reduce on-chain storage overhead and uses a laziness penalty mechanism to regulate DAP behavior. We implement a prototype of TEERollup in Golang, using the Ethereum test network, Sepolia. Our experimental results indicate that TEERollup outperforms zero-knowledge rollups (ZK-rollups), reducing on-chain verification costs by approximately 86% and withdrawal delays to a few minutes.
Erqiang Deng, Li You, Fazlullah Khan, Guosong Zhu · 8 authors
No abstract is available for this record.
Haihan Duan, Zhonghao Lin, Xiao Wu, Wei Cai
Web3 (also known as Web 3.0) metaverse is a blockchain-driven networked, decentralized, and open virtual world. The key feature of the Web3 metaverse is that the ownership of digital assets is recorded by non-fungible token (NFT) protocol on the blockchain. Thus, users are better encouraged to construct Web3 metaverse due to the ownership of their user-generated content (UGC). However, the existing UGC editors mainly face two challenges: they cannot guarantee the uniqueness of UGC; and they are hard-pressed to find a trade-off between model granularity and 3D modeling difficulty. In this article, we design a novel UGC editor for the Web3 metaverse, named MetaCube, to address these challenges. MetaCube applies an artificial intelligence (AI) method to assist the UGC creation for decreasing the 3D modeling difficulty while maintaining the model granularity. To guarantee the uniqueness of UGC, this article proposes 3D Crypto-dropout, a specially designed dropout that can utilize user information to control the UGC creation process and generate unique fine-grained 3D models. Our experimental results demonstrate that the proposed 3D Crypto-dropout can effectively guarantee the uniqueness of UGC from both numerical and human-centered evaluation. Moreover, the existing challenges and open research topics for the uniqueness of UGC are also profoundly discussed.
Lehao Lin, Haihan Duan, Wei Cai
Recently, the concept of metaverse has been rapidly emerging, which highly expands the human living space. Specifically, 3D models are at the heart of building a vast metaverse space, so a massive number of 3D models are needed. Existing 3D model libraries and platforms have achieved great results. However, most of them are unscalable, insufficiently open, inefficient to collect, and at risk of service disruption and data corruption. Therefore, we propose and implement Web3DP, a crowdsourcing platform for 3D models based on Web3 (a.k.a. Web 3.0) infrastructure. By using the decentralized blockchain technology, Web3DP has the advantages of transparency, auditability, traceability, data tamper-proof, high file transfer efficiency, and service stability. Experiments are conducted to validate the performance of the proposed platform. It illustrates that Web3DP shows better file transmission capabilities with an acceptable transaction fee to facilitate 3D model collecting and managing for metaverse, games, cultural heritage, etc.
Yifan Chen, Lei Li, Xinyu Hu, Jiahao Li · 6 authors
The use of Artificial Intelligence (AI) generators to create digital artwork as the content of Non-Fungible Tokens (NFTs) is prevalent. Typically, when minting AI-generated digital artwork into NFTs, the data of digital artwork is stored in the cloud or decentralized storage system, and a Uniform Resource Identifier (URI) or Content Identifier (CID) of the data is stored in the smart contract of NFTs to access the data. This makes AI art NFTs suffer from potential asset loss as conventional NFTs. Can AI be utilized to enhance the availability of AI-generated digital assets as NFT content? In this paper, we propose a new method for minting AI-generated digital assets into NFTs. The key idea of our approach is to store the latent codes of the generated assets on the blockchain instead of URI or CID in conventional NFTs. Here, the latent codes are intermediate variables in the process of generating digital assets by the generator and could restore the assets through the generator. Meanwhile, to be able to restore assets, the universal generator is stored on a distributed system, and its high popularity guarantees its availability. Experiments demonstrate the feasibility of our method. In addition, the integrity and the availability of assets minted by the proposed method and the existing ones are discussed, concluding that our approach has better availability while safeguarding integrity.
Kar Balan, Shruti Agarwal, Simon Jenni, Andy Parsons · 6 authors
We present EKILA; a decentralized framework that enables creatives to receive recognition and reward for their contributions to generative AI (GenAI). EKILA proposes a robust visual attribution technique and combines this with an emerging content provenance standard (C2PA) to address the problem of synthetic image provenance -- determining the generative model and training data responsible for an AI-generated image. Furthermore, EKILA extends the non-fungible token (NFT) ecosystem to introduce a tokenized representation for rights, enabling a triangular relationship between the asset's Ownership, Rights, and Attribution (ORA). Leveraging the ORA relationship enables creators to express agency over training consent and, through our attribution model, to receive apportioned credit, including royalty payments for the use of their assets in GenAI.
Xinyao Sun, Xiao Wu, Shuyi Zhang
No abstract is available for this record.
Katharotiya Krutarth, Manu Madhavan
No abstract is available for this record.
Nicola Noviello, Remo Pareschi
No abstract is available for this record.
ike, BarefootDev
The Ethereum blockchain permits the development and deployment of smart contracts which can store and execute code 'on-chain' - that is, entirely on nodes in the blockchain's network. Smart contracts have traditionally been used for financial purposes, but since smart contracts are Turing-complete, their algorithmic scope is broader than any single domain. To that end, we design, develop, and deploy a comprehensive 3D rendering engine programmed entirely in Ethereum smart contracts, called Shackled. Shackled computes a 2D image from a 3D scene, executing every single computation on-chain, on Ethereum. To our knowledge, Shackled is the first and only fully on-chain 3D rendering engine for Ethereum. In this work, we 1) provide three unique datasets for the purpose of using and benchmarking Shackled, 2) execute said benchmarks and provide results, 3) demonstrate a potential use case of Shackled in the domain of tokenised generative art, 4) provide a no-code user interface to Shackled, 5) enumerate the challenges associated with programming complex algorithms in Solidity smart contracts, and 6) outline potential directions for improving the Shackled platform. It is our hope that this work increases the Ethereum blockchain's native graphics processing capabilities, and that it enables increased use of smart contracts for more complex algorithms, thus increasing the overall richness of the Ethereum ecosystem.
Sina Osivand
Metaverse is an immersive 3D virtual environment, a true virtual artificial community in which avatars act as the user's alter ego and interact with each other. If we do not manage the hype for the metaverse, which has recently been receiving a surge in interest, the metaverse will fail to cross the chasm. This article conducts a comprehensive survey on computational arts, in which seven critical topics are relevant to the metaverse, describing novel artworks in blended virtual-physical realities. The topics first cover the building elements for the metaverse, e.g. Virtual scenes and characters, auditory, textual elements. Next, several remarkable types of novel creations in the expanded horizons of metaverse cyberspace have been reflected, such as immersive arts, robotic arts, and other user-centric approaches fuelling contemporary creative outputs.
Weiwei Jiang, Chaofan Wang, Zhanna Sarsenbayeva, Andrew Irlitti · 8 authors
We present a technique to embed information invisible to the eye inside 3D printed objects. The information is integrated in the object model, and then fabricated using off-the-shelf dual-head FDM (Fused Deposition Modeling) 3D printers. Our process does not require human intervention during or after printing with the integrated model. The information can be arbitrary symbols, such as icons, text,binary, or handwriting. To retrieve the information, we evaluate two different infrared-based imaging devices that are readily available-thermal cameras and near-infrared scanners. Based on our results, we propose design guidelines for a range of use cases to embed and extract hidden information. We demonstrate how our method can be used for different applications, such as interactive thermal displays, hidden board game tokens, tagging functional printed objects, and autographing non-fungible fabrication work.