FibRace, jointly developed by KKRT Labs and Hyli, was the first large-scale experiment to test client-side proof generation on smartphones using Cairo M. Presented as a mobile game in which players proved Fibonacci numbers and climbed a leaderboard, FibRace served a dual purpose: to engage the public and to provide empirical benchmarking. Over a three-week campaign (September 11-30, 2025), 6,047 players across 99 countries generated 2,195,488 proofs on 1,420 unique device models. The results show that most modern smartphones can complete a proof in under 5 seconds, confirming that *mobile devices are now capable of producing zero-knowledge proofs reliably*, without the need for remote provers or specialized hardware. Performance was correlated primarily with RAM capacity and SoC (System on Chip) performance: devices with at least 3 GB of RAM proved stably, when Apple's A19 Pro and M-series chips achieved the fastest proving times. Hyli's blockchain natively verified every proof onchain without congestion. FibRace provides the most comprehensive dataset to date on mobile proving performance, establishing a practical baseline for future research in lightweight provers, proof-powered infrastructure, and privacy-preserving mobile applications.
Andrei Seoev, Leonid Gremyachikh, Anastasiia Smirnova, Yash Madhwal · 11 authors
In blockchain networks, the strategic ordering of transactions within blocks has emerged as a significant source of profit extraction, known as Maximal Extractable Value (MEV). The transition from spam-based Priority Gas Auctions to structured auction mechanisms like Polygon Atlas has transformed MEV extraction from public bidding wars into sealed-bid competitions under extreme time constraints. While this shift reduces network congestion, it introduces complex strategic challenges where searchers must make optimal bidding decisions within a sub-second window without knowledge of competitor behavior or presence. Traditional game-theoretic approaches struggle in this high-frequency, partially observable environment due to their reliance on complete information and static equilibrium assumptions. We present a reinforcement learning framework for MEV extraction on Polygon Atlas and make three contributions: (1) A novel simulation environment that accurately models the stochastic arrival of arbitrage opportunities and probabilistic competition in Atlas auctions; (2) A PPO-based bidding agent optimized for real-time constraints, capable of adaptive strategy formulation in continuous action spaces while maintaining production-ready inference speeds; (3) Empirical validation demonstrating our history-conditioned agent captures 49\% of available profits when deployed alongside existing searchers and 81\% when replacing the market leader, significantly outperforming static bidding strategies. Our work establishes that reinforcement learning provides a critical advantage in high-frequency MEV environments where traditional optimization methods fail, offering immediate value for industrial participants and protocol designers alike.
Federated learning promises to revolutionize machine learning by enabling collaborative model training without compromising data privacy. However, practical adaptability can be limited by critical factors, such as the participation dilemma. Participating entities are often unwilling to contribute to a learning system unless they receive some benefits, or they may pretend to participate and free-ride on others. This chapter identifies the fundamental challenges in designing incentive mechanisms for federated learning systems. It examines how foundational concepts from economics and game theory can be applied to federated learning, alongside technology-driven solutions such as blockchain and deep reinforcement learning. This work presents a comprehensive taxonomy that thoroughly covers both centralized and decentralized architectures based on the aforementioned theoretical concepts. Furthermore, the concepts described are presented from an application perspective, covering emerging industrial applications, including healthcare, smart infrastructure, vehicular networks, and blockchain-based decentralized systems. Through this exploration, this chapter demonstrates that well-designed incentive mechanisms are not merely optional features but essential components for the practical success of federated learning. This analysis reveals both the promising solutions that have emerged and the significant challenges that remain in building truly sustainable, fair, and robust federated learning ecosystems.
Byzantine Fault-Tolerant (BFT) consensus protocols ensure agreement on transaction ordering despite malicious actors, but unconstrained ordering power enables sophisticated value extraction attacks like front running and sandwich attacks - a critical threat to blockchain systems. Order-fair consensus curbs adversarial value extraction by constraining how leaders may order transactions. While state-of-the-art protocols such as Themis attain strong guarantees through graph-based ordering, they ask every replica to re-run the leader's expensive ordering computation for validation - an inherently symmetric and redundant paradigm. We present AUTIG, a high-performance, pluggable order-fairness service that breaks this symmetry. Our key insight is that verifying a fair order does not require re-computing it. Instead, verification can be reduced to a stateless audit of succinct, verifiable assertions about the ordering graph's properties. AUTIG realizes this via an asymmetric architecture: the leader maintains a persistent Unconfirmed-Transaction Incremental Graph (UTIG) to amortize graph construction across rounds and emits a structured proof of fairness with each proposal; followers validate the proof without maintaining historical state. AUTIG introduces three critical innovations: (i) incremental graph maintenance driven by threshold-crossing events and state changes; (ii) a decoupled pipeline that overlaps leader-side collection/update/extraction with follower-side stateless verification; and (iii) a proof design covering all internal pairs in the finalized prefix plus a frontier completeness check to rule out hidden external dependencies. We implement AUTIG and evaluate it against symmetric graph-based baselines under partial synchrony. Experiments show higher throughput and lower end-to-end latency while preserving gamma-batch-order-fairness.
Tehnologija ulančanih blokova pruža siguran, efikasan i transparentan način spremanja podataka kao i rukovanja kriptovalutama. Pomoću Rust programskog jezika i Anchor okvira, razvijen je pametni ugovor za spremanje podataka o kupljenim kartama za vlak na Solana plaformi. Kako bi se omogućila daljnja komunikacija s pametnim ugovorom nakon njegovog objavljivanja, razvijena je mobilna aplikacija pomoću programskog jezika Kotlin u okruženju Android Studio.
Cryptocurrency ATMs have become a preferred payment method for scammers because they are a fast, easy, and often hard to trace way to get access to a victim's cash. 2 Yet, a large majority of U.S. adults cannot recognize cryptocurrency ATMs (i.e., they're unable to distinguish them from traditional bank ATMs).This di culty in recognizing cryptocurrency ATMs is especially pronounced among adults ages 50 and older.
Since more users are moving to cloud computing, keeping our data safe and private now matters more. Since most cloud storage is run from just one location, it is easier for attackers and causes issues if the system fails. We come up with a new way to secure cloud storage by using blockchain technology and smart contracts. Blockchain mainly allows us to have a safe and distributed record that ensures data access can be trusted. By using smart contracts, we enable people to safely access data without any help from a middleman. It means that you can see every use of data access in the system, and these actions cannot be deleted or changed. Using blockchain technology with distributed storage, we ensure that everything happening is recorded and access is regulated correctly. We also discuss a case study to illustrate how our idea helps with transparency and trust, while at the same time mentioning concerns such as how far it can be used and problems with regulations, and our solutions for these issues.
Cross-chain interoperability is essential for the next generation of decentralized finance applications, yet existing bridges suffer from security weaknesses, high latency, and fragmented trust models. This paper introduces SnapBridge, a protocol that transfers assets across heterogeneous blockchains using cryptographic state snapshots combined with optimistic verification. A snapshot aggregator collects Merkleized proofs of account states and transaction histories from the source chain. Instead of verifying all proofs on-chain, SnapBridge relies on optimistic execution: transfers proceed immediately but can be challenged within a fraud-proof window. Fraud detection is performed by light clients using succinct verification rules. We implement SnapBridge across Ethereum, Polygon, and Avalanche testnets and benchmark transfer throughput, failure handling, and gas consumption. Results show up to 3× improvement in transfer latency and a 40% reduction in on-chain verification cost compared to multisig-based bridges. The paper evaluates adversarial scenarios such as corrupted aggregators, delayed snapshots, and chain reorgs. SnapBridge provides a modular, safer alternative for cross-chain liquidity flows.
Modern economic ecosystems require radical hazard management systems that may take care of big streams of statistics without compromising on regulatory compliance and business transparency. Conventional batch-based risk assessment models exhibit intrinsic shortcomings in addressing millisecond-level market turbulence and intricate network interdependencies that define new trading environments. Sophisticated artificial intelligence platforms embedded in distributed computing environments offer transformational possibilities for real-time risk sensing and mitigation. The suggested architecture develops end-to-end risk analytics capacity via ensemble machine learning algorithms, graph contagion analysis, and explainable AI features to meet strict regulatory demands. Complex data pipelines ingest heterogeneous finance streams from worldwide exchanges, payment networks, and blockchain ledgers in tandem. Tailored graph neural networks examine systemic risk transmission patterns in connected financial institutions while retaining dynamic relationship mapping capabilities. Explainable AI integration presents version interpretability and regulatory adherence through function attribution strategies and robust audit trail retention. Cloud-local infrastructure layout helps elastic scaling throughout multi-cloud environments using fault-tolerant distributed orchestration systems. Performance assessments display large upgrades in detection latency and predictive accuracy relative to standard batch-processing strategies. The design embodies a paradigm shift towards forward-looking, adaptive, and transparent risk management functionality critical to ensuring financial stability in progressively complex market conditions
This research investigates the integration of Artificial Intelligence (AI) and blockchain technologies to develop a more robust and adaptive cybersecurity framework. Amid the growing complexity and frequency of cyber threats, traditional security systems are increasingly insufficient in ensuring data integrity, threat detection, and operational transparency. The study aims to explore how the synergy between AI and blockchain can address these limitations and enhance digital security infrastructures. A qualitative exploratory approach was employed, utilizing a Systematic Literature Review (SLR) of 42 peer-reviewed articles published between 2020 and 2025. The analysis revealed three dominant integration models: AI-based anomaly detection with blockchain-secured logging, smart contracts for automated incident response, and blockchain-based identity verification enhanced by AI behavioral analysis. The proposed framework demonstrated a high detection rate (94.3%), low response latency (0.7 seconds), and improved auditability compared to state-of-the-art approaches. These findings suggest that combining AI's predictive capabilities with blockchain’s immutable and decentralized architecture offers a more comprehensive cybersecurity solution. However, challenges such as computational overhead, energy consumption, and interoperability issues remain. The study concludes that the integrated approach not only enhances resilience and transparency but also provides a scalable foundation for future cybersecurity systems, especially in critical sectors such as healthcare, finance, and government services.
In this article, we carry out a comprehensive comparative legal analysis of the criminal policy in the field of cryptocurrency confiscation in Russia, the European Union, and the United States. The relevance of this research is determined by the rapid growth of crimes involving crypto assets (money laundering, cybercrimes, and drug trafficking) and the lack of effective mechanisms for their final confiscation and implementation in Russia, which undermines the efforts of law enforcement agencies. We aim to identify effective models of cryptocurrency confiscation based on a comparative analysis of legislation and practice in leading jurisdictions and, on this basis, to develop recommendations for improving the Russian legal framework. The methodology includes a comparative legal analysis of regulatory acts (Russian Criminal Procedure Code, EU Directive 2014/42/EU, US Code), a formal legal method, an analysis of judicial practice (Russia, USA), and doctrinal sources. The key findings can be summarized as follows: (1) the USA enjoys the most advanced system, where the U.S. Marshals Service (USMS) actively uses private exchanges to convert confiscated assets; (2) the EU has established a strong legal framework (5/6AMLD, Directive 2014/42/EU); however, implementation practices here vary among member states, combining government-owned storage and outsourced sales through licensed platforms; (3) in the Russian Federation, despite the practice of seizure and arrest of crypto assets and legislative initiatives, the legal mechanism for their confiscation and sale is lacking, making court decisions unenforceable. In order to overcome this gap in Russia, it is necessary to urgently legislate cryptocurrency as property for the purposes of confiscation in the Criminal Procedure Code of the Russian Federation, grant the Federal Service for Judicial Enforcement of the Russian Federation the authority to sell through licensed platforms, as well as to develop expert potential. Our study extends the current knowledge by detailing the technological aspects of confiscation in the EU and the USA and proposes specific ways to modernize the criminal policy of the Russian Federation.
Effective ICT governance is essential in the public sector to drive digital transformation and improve service delivery. This research investigates the corporate governance of ICT Policy Framework (CGICTPF) and Public Finance Management Act (PFMA) and State Information Technology Agency (SITA) Act governs the operational activities and strategic directions of Government Information Technology Officers (GITOs) in Eastern Cape, KwaZulu-Natal and Free State provincial administrations in South Africa. Using a comparative case study, the research draws on policy analysis and interviews to reveal governance obstacles in procurement and executive ICT engagement. KwaZulu-Natal shows progress due to strong leadership, while Eastern Cape and Free State face delays from compliance-driven cultures and bureaucracy. The study urges a balance between regulation and agility, recommending GITO empowerment through decentralized procurement and leadership development. It advances ICT governance theory by exposing multi-level implementation challenges.
Abdullah Ayub Khan, Abdul Khalique Shaikh, Roobaea Alroobaea, Abdullah M. Baqasah · 7 authors
Advanced precision oncology has the potential to revolutionize the current infrastructure of precision oncology, especially in cancer diagnosis and ongoing monitoring, owing to the strong development and advancement of the Internet of Medical Things (IoMT) and Blockchain Distributed Ledger Technology (BDLT). In order to improve cancer diagnosis accuracy and real-time patient monitoring, this paper introduces a novel Blockchain-enabled secure IoMT architecture that incorporates state-of-the-art multi-modal data fusion algorithms, such as weighted fusion. A comprehensive picture of patient health is made possible by this proposed architecture, which presents a novel mechanism for the safe, dynamic aggregation of various datasets, including as genetic portfolios, medical imaging, and wearable sensory-enabled data, as we assess the existing solutions. However, a BDLT-enabled immutable distributed ledger that uses cutting-edge encryption techniques to protect patient privacy while guaranteeing data immutability, decentralized access controls, fine-grained data availability, and traceability are among the main goals. This proposed architecture's unique context-aware data fusion algorithm greatly outperforms traditional techniques, achieving a diagnostic accuracy of 97.10%, precision of 98.25%, F1-scroe of 0.97, and sensitivity of 96.85%. Furthermore, the incorporation of BDLT enhanced security and privacy protection by eliminating single points of failure and attaining 100% immutability. When handling, organizing, and processing dynamic data, a latency of less than 250 ms is calculated. Through simulations using real-world case studies, the proposed work is tested, enhancing the system's reliability while also showcasing its scalability, energy efficiency, and robustness. Based on the examination of the simulation findings, we are able to reach parameters such as data integrity, throughput exceeding 300 transactions per second, and resource utilization efficiency optimized up to 85% in comparison to other state-of-the-art methodologies. It guarantees dependable functioning even with fluctuating computational loads. The findings demonstrate its ability to provide precise, secure, and useful insights instantly, revolutionizing real-time monitoring and cancer diagnosis.
The rapid decentralization and digitalization of local electricity markets have introduced new cyber-physical vulnerabilities, including key leakage, data tampering, and identity spoofing. Existing blockchain-based solutions provide transparency and traceability but still depend on classical cryptographic primitives that are vulnerable to quantum attacks. To address these challenges, this paper proposes Q-EnergyDEX, a zero-trust distributed energy trading framework driven by quantum key distribution and blockchain. The framework integrates physical-layer quantum randomness with market-level operations, providing an end-to-end quantum-secured infrastructure. A cloud-based Quantum Key Management Service continuously generates verifiable entropy and regulates key generation through a rate-adaptive algorithm to sustain high-quality randomness. A symmetric authentication protocol (Q-SAH) establishes secure and low-latency sessions, while the quantum-aided consensus mechanism (PoR-Lite) achieves probabilistic ledger finality within a few seconds. Furthermore, a Stackelberg-constrained bilateral auction couples market clearing with entropy availability, ensuring both economic efficiency and cryptographic security. Simulation results show that Q-EnergyDEX maintains robust key stability and near-optimal social welfare, demonstrating its feasibility for large-scale decentralized energy markets.
Son, Do Hai, Hieu, Le Vu, Khoa, Tran Viet, Alem, Yibeltal F. · 8 authors
Blockchain technology has experienced rapid growth and has been widely adopted across various sectors, including healthcare, finance, and energy. However, blockchain platforms remain vulnerable to a broad range of cyberattacks, particularly those aimed at exploiting transactions and smart contracts (SCs) to steal digital assets or compromise system integrity. To address this issue, we propose a novel and effective framework for detecting cyberattacks within blockchain systems. Our framework begins with a preprocessing tool that uses Natural Language Processing (NLP) techniques to transform key features of blockchain transactions into image representations. These images are then analyzed through vision-based analysis using Vision Transformers (ViT), a recent advancement in computer vision known for its superior ability to capture complex patterns and semantic relationships. By integrating NLP-based preprocessing with vision-based learning, our framework can detect a wide variety of attack types. Experimental evaluations on benchmark datasets demonstrate that our approach significantly outperforms existing state-of-the-art methods in terms of both accuracy (achieving 99.5%) and robustness in cyberattack detection for blockchain transactions and SCs.
New technologies, such as blockchain, are designed to address various system weaknesses, particularly those related to security. Blockchain can enhance numerous aspects of traditional banking systems by transforming them into digital, immutable, secure, and anonymous ledger. This paper proposes a new banking application ALBank, which is based on blockchain and smart contract technologies. Its functionality relies on invoking functions within smart contracts deployed on the Ethereum blockchain. This approach enables decentralization and enhances both security and trust. In this context, the paper first presents a critical analysis of existing research on blockchain and traditional banking systems, with a focus on their respective challenges. It then examines the Know Your Customer (KYC) process and its various models. Finally, it introduces the design and development of ALBank, a decentralized banking application built on the Ethereum blockchain using smart contracts. The results show that the integration of blockchain and smart contracts effectively addresses key issues in traditional banking systems, including centralization, inefficiency, and security vulnerabilities by storing critical data on a decentralized, immutable ledger, managing processes autonomously, and making transactions transparent to all users.
Maximal Extractable Value (MEV) refers to a class of attacks to decentralized applications where the adversary profits by manipulating the ordering, inclusion, or exclusion of transactions in a blockchain. Decentralized Finance (DeFi) protocols are a primary target of these attacks, as their logic depends critically on transaction sequencing. To date, MEV attacks have already extracted billions of dollars in value, underscoring their systemic impact on blockchain security. Verifying the absence of MEV attacks requires determining suitable upper bounds, i.e. proving that no adversarial strategy can extract more value (if any) than expected by protocol designers. This problem is notoriously difficult: the space of adversarial strategies is extremely vast, making empirical studies and pen-and-paper reasoning insufficiently rigorous. In this paper, we present the first mechanized formalization of MEV in the Lean theorem prover. We introduce a methodology to construct machine-checked proofs of MEV bounds, providing correctness guarantees beyond what is possible with existing techniques. To demonstrate the generality of our approach, we model and analyse the MEV of two paradigmatic DeFi protocols. Notably, we develop the first machine-checked proof of the optimality of sandwich attacks in Automated Market Makers, a fundamental DeFi primitive.
The dominant paradigm of the modern internet, built on graphical user interfaces (GUIs) and discrete web applications, forces users into a fragmented and manual process of interaction, fundamentally limiting the complexity of tasks that can be automated. This paper argues for a new architectural paradigm: an AI-powered network of autonomous digital agents that replaces manual navigation with goal-oriented, natural language-based service procurement. To establish the necessity and viability of this approach, a critical literature review of dominant service-oriented architectures is conducted. The analysis reveals that Microservice Architecture (MSA), despite its advantages, suffers from inherent complexities in communication, discovery, and data management that undermine true service autonomy. A review of its predecessors finds that Service-Oriented Architecture (SOA) was hindered by centralized bottlenecks, while the Semantic Web’s vision of a machine-readable web failed due to the rigidity and complexity of its formal, logic-based approach. Furthermore, modern Web3 architectures, while offering decentralization, are shown to have severe limitations in scalability, cost, and flexibility that make them unsuitable for dynamic agent collaboration. The paper concludes that these existing paradigms contain fundamental gaps and posits that a Multi-Agent System (MAS) architecture provides a more robust and appropriate foundation for building a truly autonomous, post-GUI digital ecosystem.
The growing reliance on digital financial services necessitates a secure, efficient, and privacy-centric approach to identity verification and Know Your Customer (KYC) compliance. Traditional identity management systems rely on centralized databases, making them susceptible to data breaches, inefficiencies, and regulatory constraints. Over 10 billion identity records have been exposed in centralized KYC breaches, leading to a 60% increase in financial fraud cases. The rise of Decentralized Finance (DeFi) has further complicated KYC compliance, requiring innovative solutions that balance privacy and regulatory requirements. This paper proposes a Web3-powered decentralized identity framework that leverages blockchain technology, self-sovereign identity (SSI), verifiable credentials (VCs), and zero-knowledge proofs (ZKPs). By eliminating reliance on centralized authorities, our system enhances data privacy, reducing personally identifiable information (PII) disclosure by 80% while ensuring compliance with AML and GDPR regulations. The integration of zk-SNARKs enables trustless identity verification with an average proof generation time of 12.5 seconds, significantly reducing the 3–5 day verification period required by traditional systems. Smart contract-based KYC automation eliminates intermediaries, cutting compliance costs by 40% and reducing fraud risk by 60%. Through comparative analysis, we highlight that decentralized KYC improves security, cost-effectiveness, and scalability compared to traditional models. Performance evaluation confirms that transaction throughput remains within acceptable blockchain limits, with gas costs stabilized at 35,000–55,000 Gwei per verification request. Despite challenges in regulatory adaptation and zk-SNARK scalability, the proposed model demonstrates the feasibility of Web3-driven identity management for trustless, privacy-preserving, and compliant financial ecosystems.
Saeed Moradi, Koosha Esmaeilzadeh Khorasani, Sara Rouhani
Interoperability is essential for transforming blockchains from isolated networks into collaborative ecosystems, unlocking their full potential. While significant progress has been made in public blockchain interoperability, bridging permissioned and permissionless blockchains poses unique challenges due to differences in access control, architectures, and security requirements. This paper introduces a blockchain-agnostic framework to enable interoperability between permissioned and permissionless networks. Leveraging cryptographic techniques, the framework ensures secure data exchanges. Its lightweight architectural design simplifies implementation and maintenance, while the integration of Clover and Dandelion++ protocols enhances transaction anonymity. Performance evaluations demonstrate the framework's effectiveness in achieving secure and efficient interoperability by measuring the forwarding time, the throughput, the availability, and their collusion impact of the system across heterogeneous blockchain ecosystems.
Neural networks increasingly run on hardware outside the user's control (cloud GPUs, inference marketplaces). Yet ML-as-a-Service reveals little about what actually ran or whether returned outputs faithfully reflect the intended inputs. Users lack recourse against service downgrades (model swaps, quantization, graph rewrites, or discrepancies like altered ad embeddings). Verifying outputs is hard because floating-point(FP) execution on heterogeneous accelerators is inherently nondeterministic. Existing approaches are either impractical for real FP neural networks or reintroduce vendor trust. We present TAO: a Tolerance Aware Optimistic verification protocol that accepts outputs within principled operator-level acceptance regions rather than requiring bitwise equality. TAO combines two error models: (i) sound per-operator IEEE-754 worst-case bounds and (ii) tight empirical percentile profiles calibrated across hardware. Discrepancies trigger a Merkle-anchored, threshold-guided dispute game that recursively partitions the computation graph until one operator remains, where adjudication reduces to a lightweight theoretical-bound check or a small honest-majority vote against empirical thresholds. Unchallenged results finalize after a challenge window, without requiring trusted hardware or deterministic kernels. We implement TAO as a PyTorch-compatible runtime and a contract layer currently deployed on Ethereum Holesky testnet. The runtime instruments graphs, computes per-operator bounds, and runs unmodified vendor kernels in FP32 with negligible overhead (0.3% on Qwen3-8B). Across CNNs, Transformers and diffusion models on A100, H100, RTX6000, RTX4090, empirical thresholds are $10^2-10^3$ times tighter than theoretical bounds, and bound-aware adversarial attacks achieve 0% success. Together, TAO reconciles scalability with verifiability for real-world heterogeneous ML compute.
Fernando Castillo, Eduardo Brito, Sebastian Werner, Pille Pullonen · 5 authors
Service Level Agreement (SLA) monitoring in service-oriented environments suffers from inherent trust conflicts when providers self-report metrics, creating incentives to underreport violations. We introduce a framework for generating verifiable SLA violation claims through trusted hardware monitors and zero-knowledge proofs, establishing cryptographic foundations for genuine trustworthiness in service ecosystems. Our approach starts with machine-readable SLA clauses converted into verifiable predicates and monitored within Trusted Execution Environments. These monitors collect timestamped telemetry, organize measurements into Merkle trees, and produce signed attestations. Zero-knowledge proofs aggregate Service-Level Indicators to evaluate compliance, generating cryptographic proofs verifiable by stakeholders, arbitrators, or insurers in disputes, without accessing underlying data. This ensures three security properties: integrity, authenticity, and validity. Our prototype demonstrates linear scaling up to over 1 million events per hour for measurements with near constant-time proof generation and verification for single violation claims, enabling trustless SLA enforcement through cryptographic guarantees for automated compliance verification in service monitoring.
Rapid advancement in generative AI and large language models (LLMs) has enabled the generation of highly realistic and contextually relevant digital content. LLMs such as ChatGPT with DALL-E integration and Stable Diffusion techniques can produce images that are often indistinguishable from those created by humans, which poses challenges for digital content authentication. Verifying the integrity and origin of digital data to ensure it remains unaltered and genuine is crucial to maintaining trust and legality in digital media. In this paper, we propose an embedding-based AI image detection framework that utilizes image embeddings and a vector similarity to distinguish AI-generated images from real (human-created) ones. Our methodology is built on the hypothesis that AI-generated images demonstrate closer embedding proximity to other AI-generated content, while human-created images cluster similarly within their domain. To validate this hypothesis, we developed a system that processes a diverse dataset of AI and human-generated images through five benchmark embedding models. Extensive experimentation demonstrates the robustness of our approach, and our results confirm that moderate to high perturbations minimally impact the embedding signatures, with perturbed images maintaining close similarity matches to their original versions. Our solution provides a generalizable framework for AI-generated image detection that balances accuracy with computational efficiency.
This study examines the role of blockchain-based smart contracts' influence on financial transparency and effectiveness in the economic activities of the emerging markets. In this study, the researchers utilised a mixed-method approach that includes a systematic literature review, comparative case studies from Africa, Southeast Asia, and Latin America, and expert interviews. The research findings evidence that the adoption of smart contracts can lower transaction costs, eliminate intermediary services, improve trust in financial systems, and serve as alternatives to the current financial systems. The results further demonstrate that smart contracts can improve financial inclusion through low-cost microfinance, insurance, and trade finance solutions, as well as enhance trust and transparency with immutable records and real-time auditing. Nevertheless, weaknesses in infrastructure, digital literacy, and regulatory uncertainty create difficulties for adoption. In addition, the study augments the existing prior research emphasising the impacts of financial technology innovation in emerging markets by offering findings that are beneficial to the market stakeholders including policymakers, financial services institutions, and technology innovators, by effectively positioning blockchain-based solutions implementation as better and viable option that can drive inclusive financial development in the emerging economies.