One of the main Web3 applications is Non-Fungible Tokens, blockchain-based certificates to keep track of the ownership of unique digital or physical assets. Nowadays, there is no standard method to evaluate an NFT, and only for a trait-based collection can we rely on the rarity score, which estimates the scarcity of the traits of the NFT. However, rarity is unsuitable for describing the price of a token in a volatile market, and it is not a good price indicator because a tokenâs price is strictly related to external unpredictable events and the interest people have in specific assets. In this paper, we propose an evaluation model called The Popularity Model , that aims to evaluate NFTs based on marketability The Popularity Model is based on a set of indices which define a dynamic, socioeconomic indicator, with an antifraud system. We formalised and compared our popularity model and the rarity score to show their differences. Finally, we propose two applicable use cases in which the popularity index can be applied. The experiments show and confirm the utility and efficacy of the proposed evaluation model.
Sangita Lade, Muhammad Parkar, Shreyas Nagarkar, Om Shintre · 5 authors
This paper focuses on the design and development of a decentralized blogging platform aimed at overcoming the limitations of existing centralized content publishing systems through the use of blockchain technology. Most mainstream blogging platforms remain vulnerable to censorship, data breaches, content manipulation, and arbitrary content removal. To address these challenges, the proposed system leverages Ethereum smart contracts for transaction validation and content management, along with the Interplanetary File System (IPFS) for secure, reliable, and distributed storage. The integration of these technologies ensures immutability, author verification, and strong resistance to censorship. Additionally, the platform incorporates an incentive mechanism that rewards content creators through a token-based economy and wallet-driven identity management. The system was evaluated on the Ethereum testnet and demonstrated significant improvements in content persistence, ownership verification, and censorship resistance compared to traditional platforms. Performance analysis indicates stable transaction confirmation times and efficient storage utilization, with potential for further optimization of gas consumption. User interface testing further confirms that the platform effectively abstracts blockchain complexity, offering a user-friendly experience. Overall, this work presents a practical Web3-based content publishing solution that supports creator empowerment and content integrity in a trustless digital environment.
Digital forensic investigation in 2025 faces unprecedented challenges posed by the convergence of decentralized web technologies (Web3), adversarial generative AI systems, and darknet infrastructure. Traditional attribution and evidence preservation methodologies prove in-sufficient when adversaries exploit blockchain immutability, synthetic media generation, and privacy-enhancing technologies to obscure malicious intent. This paper in-traduces SHARD (Shadowed and Silicon Hybrid Attribution and Reconstruction Diagnostic), a multi-modal forensic framework designed to recover, correlate, and at-tribute malicious artifacts across distributed ledger systems, synthetic content generators, and anonymized net-works. Through systematic analysis of 47 real-world cybercriminal cases and forensic evaluation against 12 at-tack vectors, SHARD achieves 89.2% attribution accuracy while reducing investigative timelines by 64% com-pared to conventional methods. We present novel techniques for blockchain temporal analysis, deepfake prove-nance tracking, and Tor-exit node correlation. The frame-work integrates machine learning-based anomaly detection with cryptographic verification to distinguish legitimate decentralized activity from adversarial manipulation. Our contributions include: (1) a formal threat model encompassing Web3 forensics; (2) a hybrid architecture combining on-chain and off-chain analysis; (3) algorithmic innovations for synthetic media fingerprinting; and (4) extensive empirical validation against contemporary attack scenarios. This work addresses a critical gap in digital forensics as investigative techniques must evolve alongside the technological infrastructure that criminals exploit.
The user-ownership model of Web3 commerce is widely viewed as a potential paradigm shift for the digital economy, yet its macroeconomic implications remain under-quantified within a unified, dynamic, and parameterized framework. This paper develops a tractable dynamic macroeconomic model of a âwealth flywheelâ featuring two feedback channels. The income loop operates through profit-backed user rebates that raise income-equivalent purchasing capacity and stimulate consumption. The asset loop operates through consumption-driven profit and valuation growth, which expands household wealth under user ownership and feeds back into consumption via wealth effects. In a static setting, the paper derives a closed-form consumption multiplier and a corresponding stability condition. Aggregate consumption responds proportionally to an exogenous income impulse, and the system is stable if the combined strength of rebate-induced consumption feedback and wealth-effect amplification remains below unity. The static mechanism is then embedded into a global multi-period simulation framework with time-varying Web3 penetration, finite-horizon household deposit reallocation into consumption, and endogenous valuation paths. Using illustrative parameterizations, the paper simulates trajectories for global real GDP, equity market capitalization, household wealth, and inflation under neutral and aggressive adoption scenarios. The analysis further examines distributional implications when capitalization gains are directed toward user cohorts with higher marginal propensities to consume. The framework provides a parsimonious diagnostic for stability in mechanism design and contributes to macro-prudential discussions of self-reinforcing growth dynamics. Importantly, the analysis abstracts from collateralized borrowing, leverage, rehypothecation, and other financial intermediation channels. All amplification effects in the model arise from ownership structure and wealth effects rather than from credit-driven financial accelerators.
How may digital platforms be redesigned to better serve the interests of the artists whose creative work gives them value? An artist- and user-owned streaming platform is proposed that would decentralize control and redistribute revenue from corporations to creators. Using Web3 infrastructure, the model enables direct artist payment through blockchain-based transactions that scale based on user consumption, minimizing fees and ensuring transparency. The design also emphasizes community governance and localized music discovery to encourage the regrowth of music culture. By reducing reliance on profit-driven intermediaries, the system aims to create a sustainable environment where independent artists can thrive. Spotify exemplifies how a platformâs designed-in incentives can perpetuate exploitation. The social construction of technology framework suggests that Spotifyâs ownership model, pro- rata payment system, and algorithmic design prioritize shareholder value over fairness. Spotifyâs supposed mission to âunlock the potential of human creativityâ is undermined by its own architecture, which locks artists into dependency. Together, these projects show that achieving fairness in a digital music economy requires not only reforming compensation models but rethinking the infrastructures that define creative labor itself.
Even in this day and age, when digital technologies are becoming more and more prevalent, it is still extremely important for democratic systems to maintain the honesty and openness of their voting procedures. This article introduces NextGenVote, a decentralised online voting platform developed to address the security, transparency, and confidence issues traditional electronic voting systems face. Automation of election operations, including voter registration, candidate administration, ballot casting, and result computation, is achieved through smart contracts written in the Solidity programming language. The system is built on the Ethereum blockchain. MetaMask is a React-based frontend that uses Web3.js to connect to the blockchain. MetaMask is responsible for ensuring that user authentication and transaction signatures are secure. Therefore, to prevent unauthorised manipulation, the platform utilises a role-based access control approach that clearly distinguishes between administrative capabilities and voter credentials. NextGenVote assures that election results are tamper-proof, traceable, and auditable. It was deployed and tested in a local blockchain environment powered by Ganache. The system provides a solid foundation for scalable, secure, and transparent digital elections by eliminating centralised intermediaries and relying solely on processes executed on the blockchain.
Organ transplantation is life-saving but faces major ethical, logistical, and trust-related challenges. Centralized organ donation workflows are susceptible to delays, data tampering, and opaque allocation decisions. We present a full-stack Blockchain-Based Organ Donation Management System that integrates Ethereum smart contracts to deliver immutable audit trails and transparent allocation events while keeping sensitive clinical data encrypted off-chain. The prototype couples a React + Vite frontend with a Flask backend (PostgreSQL, Web3.py) and minimal on-chain logic in Solidity. Evaluation on a private Ethereum testbed shows correct matching, low end-to-end latency on the local network, and acceptable gas usage when heavy computation is performed off-chain. The design is extensible to Layer-2 scaling and privacy-preserving primitives for real-world deployment.
Vivekrabinson K, C Yogesh, Indra Kumar M, Hari Suriya K · 6 authors
The rapid progress of generative AI has already seen the rise of highly realistic artificial and deepfake content that has created a problems related to trust on information, privacy, issues related to cyber security loss of general trust. This research introduces TrueSightQ, a unified full stack web application framework through with multimodal detection of AI generated content through use of Quatnum enhancement and Web3 Integration. This system is a hybrid between heuristic and deep learning methods with the added feature of GPU accelerated training combined with quantum advantage classifiers. The trustworthiness and transparency of blockchain technology, as well as IPFS storage and Ethereum Smart contract to improve verification process. The model is further enhanced with modality wise fusion and, decentralized trust based mechanisms for the defense mechanism to adversarial attack. Results show that in general TrueSightQ significantly outperforms standard unimodal detectors overall, with additional gains to verifiability, precision and interpretability demonstrating how the multimodal and decentralized methodology within the model mitigates the issues of AI generated content very efficiently.
The increasing use of decentralized finance (DeFi) accelerates the demand for trustless, secure mechanisms for crosschain token exchange. This paper outlines a complete model for atomic token swaps based on the Hashed Timelock Contract (HTLC) scheme, allowing for intermediary-free token exchanges across disparate blockchain systems. The system makes use of the local blockchain simulation framework, Ganache, to design and test cross-chain interactions in a sandbox environment. To improve the decision-making capabilities for users, a real-time cryptocurrency price forecasting subsystem is added which utilizes machine learning models to analyze and predict the market and its volatility. Additionally, the system harnesses Generative AI capabilities through prompt engineering to tailor investment advice for individual users by analyzing the market, their preferred risk level, expected returns, and provide investment strategies aligned with users' preferences. Apart from sophisticated trading algorithms, the solution also offers a simple dashboard for market price monitoring and performs rapid token swaps at the user's command. Smart contracts are implemented using Solidity, token and price feeds are ports to Web3.js, predictive analytics is done in Python, while the frontend and backend are structured in Next.js alongside Node.js. System testing validates hypotheses on the provision of secure cross-chain swaps within one transaction without compromising.
ABSTRACT In the contemporary digital landscape, high-profile individuals including celebrities, executives, political leaders, and public officials face unprecedented threats from online impersonation, sophisticated misinformation campaigns, AI-generated deepfakes, and fraudulent social media profiles. The convergence of generative artificial intelligence technologies and social media platforms has dramatically expanded the attack surface, enabling malicious actors to create synthetic identities, manipulate multimedia content, and spread false narratives with alarming ease and speed. Existing security solutions remain fragmented, requiring extensive manual intervention and lacking the capability for real-time monitoring and automated threat response, thereby leaving critical gaps in digital protection for vulnerable public figures. This research paper presents GuardIQ, an integrated, fully automated, end-to-end VIP Threat Detection and Monitoring Platform that combines post-quantum cryptography, multi-factor biometric authentication, artificial intelligence-powered threat detection, and blockchain-based evidence preservation. The platform architecture is built upon seven core pillars: quantum-secure biometric registration utilizing Kyber Key Encapsulation Mechanism (KEM), real-time threat detection engine monitoring multiple social media platforms, AI-powered content verification distinguishing authentic media from AI-generated deepfakes, automated fake profile detection comparing discovered accounts against registered handles, live analyzer for instant authenticity verification, immutable evidence collection using Web3 technologies, and unified dashboard providing comprehensive threat intelligence visualization. GuardIQ employs CRYSTALS-Kyber post-quantum cryptographic algorithms (Kyber512 for lightweight mobile endpoints and Kyber768/1024 for enterprise deployments) combined with AES-256-GCM symmetric encryption to ensure quantum-resistant data protection. The biometric registration module captures facial recognition data, voice patterns, gesture signatures, and official social media handles, all protected through quantum-safe encryption. Large Language Models (LLMs) integrated within the threat detection engine perform real-time classification of suspicious content, achieving 92-97% accuracy in identifying impersonation attempts, misinformation campaigns, and image misuse across platforms including Twitter, Facebook, Instagram, and LinkedIn. The AI content detection module leverages advanced deep learning architectures including Convolutional Neural Networks (CNNs) for image analysis, Recurrent Neural Networks (RNNs) for sequential pattern detection, and transformer-based models for multimedia authenticity verification. Experimental results demonstrate the system's capability to distinguish AI-generated content from authentic material with confidence scores exceeding 94%, providing early detection of deepfakes and synthetic media targeting VIP credibility. The fake profile detection algorithm analyzes multiple parameters including account creation timestamps, username patterns, biographical information, follower-to-following ratios, engagement metrics, and posting behavior patterns to identify fraudulent accounts with 89% precision. Evidence collection is facilitated through Web3-based blockchain infrastructure ensuring tamper-proof, immutable storage of all flagged incidents, suspicious posts, and detected impersonations. This cryptographically verifiable evidence chain supports legal proceedings and investigative actions by providing irrefutable proof of malicious activities. The unified dashboard aggregates threat intelligence from all modules, presenting real-time alerts, authenticity scores, risk assessments, and recommended remediation actions through intuitive visualizations requiring minimal manual oversight. Performance evaluation reveals that post-quantum TLS handshakes introduce only 5-10 milliseconds additional latency compared to classical TLS implementations, demonstrating practical feasibility for production deployment. The automated threat detection pipeline reduces incident response time by 72% compared to manual monitoring approaches, while the quantum-resistant encryption framework ensures long-term security against emerging quantum computing threats. System architecture supports horizontal scalability through microservices deployment, containerization using Docker and Kubernetes orchestration, and cloud-native infrastructure compatible with AWS, Azure, and Google Cloud Platform. This research addresses the urgent need for comprehensive digital protection solutions in an era where AI-generated content, quantum computing capabilities, and sophisticated social engineering attacks converge to create unprecedented risks for public figures. GuardIQ represents a paradigm shift from reactive security measures to proactive, automated threat intelligence platforms capable of defending high-profile individuals against modern digital adversaries while maintaining usability, scalability, and legal compliance. Keywords : VIP Protection, Post-Quantum Cryptography, Kyber KEM, Deepfake Detection, AI Content Verification, Biometric Authentication, Threat Intelligence, Social Media Monitoring, Blockchain Evidence, Web3 Security, Impersonation Detection, Misinformation Prevention, Large Language Models, Zero- Trust Architecture, Quantum-Safe Encryption, Identity Verification, Automated Security Response, Digital Reputation Management
This project aims to create, utilize, and evaluate a blockchain-based solution for event ticketing, addressing issues such as fraud, duplicate tickets, excessive fees from intermediaries, and unfair resale practices prevalent in traditional ticketing systems. The project develops a powerful stack of Solidity smart contracts on Ethereum/Polygon blockchains to standardise creation, ownership transfer, and resale policies for tickets. At the same time, a React.js frontend is being developed to create a fast and agile ticket purchase, reservation, and store transfer interface. This interface utilizes Metamask wallet authentication in a Node.js and Express backend, which connects to the blockchain using Web3.js. For decentralization and immutability, ticket metadata is recorded on the IPFS system, and its performance is verified by means of QR code scanning modules integrated into the IoT-based entry gates for real-time validation. The project was deployed on Ganache, and it has been tested on the Polygon Mumbai testnet for performance analysis under real-world conditions. According to the report, there is error-free validation for over 500 concurrent transactions, an 8â12 second average time to confirm a transaction, and an average 70% reduction in counterpart gas fees on Polygon compared to Ethereum. The system addresses user access, transparency, duplicate elimination, and secure resale tracking. This work fills the void between theoretical models and practical solutions, with a demonstration of such a scheme and quantifiable results, proving the applicability of blockchain ticketing systems for transparent and scalable event management that prevents fraud.
Traditional financial lending systems face ongoing issues. These include too much involvement from middlemen, limited financial inclusion, a lack of transparency, and centralized decision-making. Such problems push unbanked and underbanked populations to the margins. This paper presents a blockchain-based decentralized peer-to-peer (P2P) credit system built on the Ethereum network. It uses smart contracts and Decentralized Autonomous Organization (DAO) frameworks that are transparent, secure, and accessible worldwide. Traditional financial institutions still rely heavily on centralized models. These models involve many intermediaries, slow processes, and high operational costs. As a result, many people have restricted access to financial services. According to the World Bank's Global Findex Database, around 1.7 billion adults are unbanked and shut out from essential financial systems. This exclusion often stems from strict eligibility requirements, lack of credit history, and absence of collateral. It not only hampers individual financial growth but also limits broader economic inclusion in developing nations. The proposed model eliminates the need for centralized institutions. It speeds up transactions, increases transparency, and allows for borderless digital lending. The system architecture uses Solidity for smart contract development, Web3.js for blockchain interaction, and a React-based user interface to create a robust full-stack decentralized application (DApp). The framework features automated asset collateralization, AI-driven interest rate modeling, trust-score-based borrower assessment, and community governance mechanisms. Performance comparisons show marked improvements in transaction speed, efficiency, and financial access compared to traditional lending models. The prototype achieves a 65% reduction in infrastructure costs and an 80% faster loan settlement time across different regions.
Quang Huy Duong, Carlos F.A. Arranz, Mao Xu, Li Zhou · 5 authors
The rapid transition to electric vehicles has intensified challenges in electric vehicle battery (EVB) closed-loop supply chains (CLSC), particularly regarding material traceability, supply chain transparency, and recycling efficiency. While decentralised technologies, particularly Web3 and Metaverse, offer promising solutions, their integration into EVB CLSC remains fragmented and insufficiently examined. We introduce an Operational Decentralisation Framework enabling a systematic analysis of centralised operations and a critical evaluation of decentralised alternatives as transformational forces. By adopting a holistic perspective, the framework equips firms with strategic guidance for transitioning from centralised structures to decentralised ecosystems. We analyse 588 academic articles and 1,168 industry documents through two advanced text mining techniques â Dynamic Latent Dirichlet Allocation and Burst Detection. Web3 and metaverse can potentially reconfigure the design, manufacturing, end-of-life diagnostics, procurement, waste management, load balancing, capacity planning, inventory management and service operations of two key areas: (1) EVB CLSC operations and (2) EVB circular energy/grid operations. We also found that while blockchain and digital twins show established applications, Web3 and Metaverse applications face significant barriers, including scalability, technology complexity, and expertise gaps, despite their great potentials. Therefore, we propose four visionary models integrating Web3, Metaverse, and AI technologies that have the potential to overcome existing barriers and enable transformative decentralisation. Extending the TOE framework, the study contributes to the theory by developing an integrated framework for evaluating decentralised technology adoption in EVB CLSCs. For practitioners, we provide actionable insights and pathways for technology implementation across different CLSC stages and guidance for addressing key adoption barriers.
This study proposes a structural model for understanding digital trust in smart-market environments by comparing the market-based trust architecture of Korea and the state-based trust architecture of China. Although both countries rely on similar technological foundationsâblockchain, data infrastructure, AI systems, and CBDCâtheir institutional path dependencies and regulatory philosophies have produced divergent trust mechanisms. To explain these differences, the study introduces the 4-Layer Trust Architecture (4LTAâSeo), comprising incentives, rule enforcement, verification (data/AI), and institutional linkage. This framework conceptualizes tokens as digital institutions that integrate these layers to automate trust formation and oversight.Methodologically, the research applies Qualitative Comparative Analysis (QCA) using policy documents, technical whitepapers, and regulatory texts from both countries. It incorporates Zhang & Wangâs DTI (DataâAlgorithmâRiskâPrivacy) framework to compare how information architectures shape verification dynamics and trust costs. The study analyzes how institutional configurations rearrange the weighting and function of each trust layer, producing different stability and cost outcomes.Findings are expected to show that Koreaâs market-driven architecture emphasizes incentives and behavioral inducement, while Chinaâs state-driven model prioritizes rule enforcement and systemic integration. The research clarifies how tokens function as "units of trust" only when embedded within institutionally coherent architectures. Ultimately, the study offers structural insights for reinstitutionalizing trust in digital systems, with implications for Web3 governance, CBDC design, and digital public administration.
The aim of this bachelorâs thesis is to clarify the key similarities and differences between physical and virtual real estate markets, focusing on marketplaces, transaction processes, market participants and value formation. The study is conducted as a literature review comparing the established, nationally regulated practices of traditional real estate markets with the global and still developing structure of blockchain-based metaverse markets. The findings show that although both markets share fundamental value drivers such as location, scarcity and income potential, the mechanisms behind these factors differ significantly. Institutional investors play a stabilizing role in physical markets, whereas metaverse markets remain fragmented and speculative. The results complement existing research and provide practical insights for professionals in both real estate and Web3 environments.
Understanding the economic intent of Ethereum transactions is critical for user safety, yet current tools expose only raw on-chain data, leading to widespread "blind signing" (approving transactions without understanding them). Through interviews with 16 Web3 users, we find that effective explanations should be structured, risk-aware, and grounded at the token-flow level. Based on interviews, we propose TxSum, a new task and dataset of 100 complex Ethereum transactions annotated with natural-language summaries and step-wise semantic labels (intent, mechanism, etc.). We then introduce MATEX, a multi-agent system that emulates human experts' dual-process reasoning. MATEX achieves the highest faithfulness and intent clarity among strong baselines. It boosts user comprehension by 23.6% on complex transactions and doubles users' ability to find real attacks, significantly reducing blind signing.
Understanding the economic intent of Ethereum transactions is critical for user safety, yet current tools expose only raw on-chain data or surface-level intent, leading to widespread "blind signing" (approving transactions without understanding them). Through interviews with 16 Web3 users, we find that effective explanations should be structured, risk-aware, and grounded at the token-flow level. Motivated by these findings, we formulate TxSum, a new user-centered NLP task for Ethereum transaction understanding, and construct a dataset of 187 complex Ethereum transactions annotated with transaction-level summaries and token flow-level semantic labels. We further introduce MATEX, a grounded multi-agent framework for high-stakes transaction explanation. It selectively retrieves external knowledge under uncertainty and audits explanations against raw traces to improve token-flow-level factual consistency. MATEX achieves the strongest overall explanation quality, especially on micro-level factuality and intent quality. It improves user comprehension on complex transactions from 52.9% to 76.5% over the strongest baseline and raises malicious-transaction rejection from 36.0% to 88.0%, while maintaining a low false-rejection rate on benign transactions.
The study is grounded on the significant shifts, central values, and increased influence of Blockchain Technology on the industries. It addresses Blockchain Technology starting theoretically as a cryptographic concept of the beginning through to its contribution as a primarycomponent of decentralized computing (Web3). The discussion begins as the key issues are examined, namely, decentralized agreement, cryptographic hashing, and immutability. Such subjects enable the Blockchain Technology to gain trust in cases where mediators were being used in the past. Moreover, the research considers the impacts of Blockchain Technology on such critical industries as Decentralized Finance (DeFi), supply chain management, and decentralized governance (DAOs).
This chapter explores interoperability as the foundation for functional, inclusive and emotionally resonant digital experiences â from smart homes to the metaverse. Through critiques of Internet of Things (IoT), Non-Fungible Tokens (NFTs) and Web3, it argues for human-centred design that prioritises discoverability, dignity and decentralised control. Drawing lessons from games like Bury Me My Love and platforms like Spatial , it shows how autonomy, mastery and purpose (AMP) can guide persistent virtual worlds. Interoperability is reframed not just as a technical goal, but also as a social, economic and ethical imperative â crucial to everything from refugee support to metaverse monetisation.
This chapter traces the evolution of video game tech stacks from early home computing to modern metaverse platforms. Blending personal narrative with technical insight, it explores programming languages, modular design and infrastructure across MMOs, and Web2 and Web3 ecosystems. Games like Mined-Out , RuneScape , Jetpac and Tomb Raider illustrate how early innovation laid the groundwork for today&s;s persistent digital experiences. The chapter argues that game developers, with their systems thinking and user-centred design, are natural architects of the metaverse.
With the rapid evolution of the Decentralized Web (DWeb), decentralized technologies have paved new avenues for Web3 applications and the authentication of digital assets. Among them, Non-Fungible Tokens (NFTs) have gained significant popularity due to their immutability and uniqueness, reshaping the landscape of artistic creation, marketing, and intellectual property protection. However, current blockchain-based NFT implementations still face core challenges within decentralized architecture: how to maintain decentralization while ensuring the visual uniqueness of digital assets and reducing storage costs. The rampant issue of duplication undermines the scarcity of digital art and erodes market confidence in copyright authenticity. Moreover, high gas fees and energy consumption further hinder the widespread adoption of NFTs, while reliance on external storage solutions like InterPlanetary File System (IPFS) introduces risks of data instability and loss. To address these challenges, this article presents the UniqueNFT framework, a novel architecture that deeply integrates blockchain oracles with decentralized storage verification mechanisms. The framework achieves three key technological breakthroughs: Using image inversion and generation techniques based on Encoder for Editing (E4E) and StyleGAN3, it extracts compact and expressive semantic features from NFT images, enabling efficient data compression and significantly reducing on-chain storage volume; The Crypto-Mask algorithm, by utilizing the hash value of blockchain user information (user-controlled SHA-256 digest of Ethereum address, user nickname, and registration time), ensures the visual uniqueness of NFTs; A smart contract extension compatible with the ERC721 standard, demonstrating UniqueNFTâs seamless integration within the blockchain ecosystem. By leveraging the technologies of the Decentralized Web, our framework represents an important step forward in enhancing the security and uniqueness of digital assets. It not only innovatively resolves the issues of NFT duplication and homogenization but also injects new vitality and long-term momentum into the creation of a trusted, sustainable blockchain-based digital asset ecosystem.