Henry Ohiani Ohize, Adeiza James Onumanyi, Lukman Adewale Ajao, Buhari Ugbede Umar · 9 authors
Despite significant advances in electronic voting technologies, voter accreditation in many electoral systems remains vulnerable to identity fraud, database tampering, equipment failure, and centralized security breaches. Existing accreditation solutions often rely on single-modal biometric authentication and centralized architectures, limiting their robustness, transparency, and public trust. This paper proposes a Blockchain-based Bimodal Voter Accreditation System (Block-BVAS), together with a practical framework for its deployment in electronic voting systems. The proposed system integrates multimodal biometric authentication using facial and fingerprint recognition with a private Ethereum blockchain and conventional cryptographic mechanisms to provide secure, tamper-resistant, and auditable voter accreditation to provide secure, decentralized, and tamper-resistant voter accreditation. A Raspberry Pi 5 serves as the embedded processing platform, demonstrating the feasibility of implementing the framework on cost-effective hardware. By combining distributed-ledger technology with encrypted biometric verification, the proposed architecture enhances the integrity, confidentiality, and immutability of election-related records while addressing limitations associated with single-factor authentication and conventional centralized record management. Experimental evaluation of the biometric authentication module performed effectively, with fingerprint recognition achieving an average authentication accuracy (AA) of 97.8% and facial recognition averaging 95.1%. The blockchain storage overhead (BSO) displayed a near-linear growth pattern relative to the number of transactions, consistent with theoretical expectations for blockchain architectures. Reliability analysis indicated system uptime exceeding 95%, with only minimal operational failures recorded during the test period. This blockchain implementation further demonstrated reliable transaction processing and secure record management, indicating the effectiveness of the proposed Block-BVAS in enhancing the security, transparency, and trustworthiness of electronic voter accreditation.
Open access
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
Non-fungible tokens (NFTs) and other Web3 multimedia are typically stored off-chain because image and video assets exceed practical on-chain storage budgets, creating a gap between immutable ownership records and long-term media availability. This paper proposes a collection-level neural compression approach that converts an entire NFT collection into a single compact decoder. The decoder maps a token's integer index directly to its reconstructed image, and is intentionally trained to memorize the collection so that no per-image latent codes are stored. To minimize the decoder's on-chain footprint, we parameterize weights in the frequency domain, progressively prune high-frequency coefficients via zigzag-ordered masking, and apply run-length plus Huffman entropy coding to the resulting sparse parameters. Across three 10,000-image NFT benchmarks, Bored Ape Yacht Club (BAYC), Azuki, and CryptoPunks, the compressed artifacts are reduced to 7.63 MB, 17.09 MB, and 3.33 MB, respectively. These artifacts achieve up to \(177\times\) smaller size than PNG while maintaining high reconstruction quality, measured by Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), with PSNR \(\geq\) 33 dB and SSIM \(\geq\) 0.967. We further report Ethereum storage gas estimates showing that collection-level on-chain deployment becomes feasible at \(10^{3}\) – \(10^{4}\) USD under representative fee assumptions, reducing on-chain deployment cost by 58.6%–99.4%. The main contribution is a collection-level compressed decoder that serves as the deployable on-chain storage artifact, removing dependence on external media hosts and avoiding per-image latent storage. These results demonstrate a practical path to self-contained, on-chain availability of large NFT multimedia collections without relying on external storage networks.
Advanced Data Compression Techniques
Advanced Steganography and Watermarking Techniques
Digital image steganography has evolved from traditional rule-based techniques to advanced data-driven frameworks enabled by deep learning. However, existing surveys remain fragmented, often focusing on limited aspects while overlooking emerging paradigms such as blockchain-integrated and quantum-based approaches. This paper presents a comprehensive and systematic review of digital image steganography following the PRISMA 2020 guidelines, covering studies published between January 2015 and April 2026 across six major scientific databases. From an initial pool of 26,539 records, 83 relevant studies were selected through a rigorous two-stage screening process. The review provides a unified analysis of steganographic techniques by examining five dimensions: structural evolution and taxonomy, algorithmic modifications and hybridisation, application domain mapping, integration of emerging technologies, and future research trends. Comparative evaluation indicates that deep learning-based methods achieve 18–23% higher steganalysis resistance than classical approaches, whereas classical methods retain a 5–8 dB PSNR advantage. The quantitative synthesis further confirms the inherent capacity–imperceptibility–security trilemma, wherein no reviewed technique simultaneously achieves $$\text {PSNR} > 42$$ dB, embedding capacity $$> 4$$ bpp, and detection error rate $$> 0.48$$ . Six open challenges and seven future research directions are identified and grounded in evidence from the included studies, with explainable steganography, quantum-resistant frameworks, and latent diffusion model integration emerging as the most critical priorities for advancing the field toward practical and secure deployment.
Open access
Advanced Steganography and Watermarking Techniques
With the rapid proliferation and interconnection of massive IoT devices, efficient and secure identity authentication has become a crucial prerequisite for ensuring communication security. Establishing trust among mutually untrusted devices remains a key research focus. Leveraging its tamper-resistance and traceability, blockchain technology has emerged as a foundational infrastructure for building trustworthy identity management systems. However, existing blockchain-based identity authentication schemes face critical challenges in large-scale IoT environments, including low authentication efficiency, complex certificate management, and risks of user privacy leakage. Achieving a balance among authentication efficiency, certificateless key management, and privacy protection remains a pressing challenge. In this paper, we propose a certificateless identity authentication scheme based on blockchain sharding. The scheme employs blockchain sharding to parallelize identity authentication across multiple shards, significantly enhancing overall efficiency. Within each shard, a certificateless public key cryptography (CL-PKC) scheme is adopted to eliminate certificate issuance and enable key generation via user interaction, thereby reducing key management overhead and improving security. For cross-shard authentication, a registration-based encryption (RBE) mechanism is utilized, allowing users to authenticate via their identity after registration. Any verifier can confirm the legitimacy of the authentication message solely based on the registration information and the user ID, ensuring transparency and public verifiability. Furthermore, a zero-knowledge proof-based verifiable credential (VC) selective disclosure mechanism is introduced, enabling users to reveal only the minimal necessary information required for authentication while protecting sensitive identity attributes. Experimental results demonstrate that the proposed scheme maintains high throughput under high-concurrency scenarios while effectively preserving user privacy.
Open access
2 source records
Cryptography and Data Security
Privacy-Preserving Technologies in Data
Advanced Steganography and Watermarking Techniques
Abstract A Non-Fungible Token (NFT) is a digital asset representing ownership or proof of authenticity of a unique digital item. NFTs are used for various purposes, including digital art, collectibles, virtual real estate, and tokenizing unique digital or physical items, and have introduced new dimensions to digital ownership and enabled individuals to tokenize unique digital assets using blockchain technology. Although NFTs offer exciting opportunities, they suffer from interoperability, high energy consumption, piracy, ownership control, and security issues. In this paper, an idea has been proposed in which any image, pdf file, or video file can be converted to an NFT and owned. We have used the ERC-721 standards, the PoS consensus protocol, and a smart contract to address the challenges. The proposed framework provides a step-by-step guide to create, list NFTs and maintain secure ownership where metadata are stored on IPFS, which generates a unique URL. This URL is then logged on the blockchain, saving time and costs. The created NFTs are interoperable among various applications and frameworks. Smart contract has been formally verified using Slither and tested against vulnerability using Smart Contract Weakness Classification (SWC) standards. Performance of the proposed system has been measured in terms of execution cost, latency, and throughput including statistical indicators like variance and confidence intervals. Minting cost has been compared with the similar network condition.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.
Biometric Identification and Security
Advanced Steganography and Watermarking Techniques
In client-server applications such as copyright protection and content moderation, learning-based perceptual hashing compresses images into compact binary codes whose Hamming distances approximate perceptual similarity. Clients then transmit these codes to servers for comparison. However, this approach faces dual challenges: algorithmically, how to effectively balance robustness and discriminability while mitigating bit imbalance issues; protocol-wise, transmitting these hashes compromises client privacy through content inference and cross-platform user tracking. To address these challenges, we propose a trustworthy privacy-preserving framework that integrates deep hashing with zero-knowledge proofs. The framework comprises: (1) A robust deep hashing module that generates discriminative binary codes by optimizing a composite objective function composed of the Angular Triplet and quantization losses, while using a multi-scale strategy to correct bit imbalance. (2) A privacy-preserving similarity comparison protocol based on Sumcheck and Logarithmic Lookup, which enables clients to locally prove batch Hamming distance relationships against public dataset entries without disclosing their hash values. We conducted comprehensive evaluations to demonstrate the practicality and efficiency of our design compared to existing schemes. Source code is available at https://github.com/mengdehong/zkph.
Advanced Steganography and Watermarking Techniques
Cheri Venkata Sai, Gurijela Pavan, Pittala Abhirameshwar, S. Suma
These come hand in hand with unprecedented levels of complexity in copyrighting and mon- etizing creations. In general, this protects the copyrights under the existing framework, which are cen- tralized, expensive, and beyond the reach of any independent creator. This paper presents an innovative blockchain-based framework for image copyrighting and social crypto monetization by using blockchain technologies such as Ethereum smart contracts and the InterPlanetary File System (IPFS). The proposed framework enables creators to publish digital images, calculate cryptographic proofs of image ownership with the SHA-256 hashing algorithm, store images in IPFS, and record metadata into the blockchain with unchanged timestamps. In addition, the platform supports “Like to Earn”, where public engagement for viewing is translated directly into rewarding creators with cryptocurrencies via smart contracts. The proposed framework adopts Web3 technologies to enable secure signing of all transactions with fraud prevention using the Elliptic Curve Digital Signature Algorithm (ECDSA) technique through MetaMask wallet authentication. Experimental evaluation of the proposed framework confirms that it can remove duplicate uploads, promptly verify image ownership, and enable social monetization of cryptocurrencies in a secured way.
Open access
2 source records
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Document authentication remains a pressing challenge in various domains, including financial services, academic credentialing, healthcare, and supply chain management. Existing centralized verification systems are vulnerable to manipulation, inefficiency, and limited transparency. Blockchain technology, with its immutability and tamper-resistant capabilities, offers a strong decentralized alternative; however, many current implementations lack structured, issuer-bound relationships for documents. This paper proposes a blockchain-based model that leverages a hierarchical token structure to authenticate and trace the provenance of high-value digital documents, with a focus on financial records. The model introduces the concept of an issuer-bound parent token and document-linked child tokens, enforcing a structured trust relationship between a legitimate institution and the documents it issues. By combining on-chain cryptographic hashing with off-chain file references, the approach is designed to balance verifiability with scalability. We implement a proof-of-concept using Ethereum-compatible smart contracts on a permissioned blockchain and evaluate it in a consortium-style financial setting. Our functional analyses demonstrate the model’s ability to ensure document integrity, provenance, and resistance to document fraud. This work offers a practical and extensible foundation for secure digital document authentication and verification in financial and other trust-sensitive settings.
Open access
Blockchain Technology Applications and Security
Cryptography and Data Security
Advanced Steganography and Watermarking Techniques
In this paper, we presented an e-Registry prototype that builds on the decentralized and tamper-evident nature of Ethereum to authenticate digital documents. The system is a gas-efficient Smart Contract on the Ethereum blockchain that stores SHA-256 hashes of documents, so your sensitive files are secure with us, but we don't know what they are! Accessible through a web-based user interface using MetaMask for transaction signing and the Web Crypto API for client-side hashing of documents offers an intuitive privacy-preserving process. Finally, system diagnostics and security reports can be generated using Python-based tools and hashed to store on the blockchain for validation. Running on the Ethereum Sepolia testnet, the system provides practical efficiency, with transaction fees of 0.0005-0.001 SepoliaETH and verification delay less than 3 seconds. This provides an alternative to traditional centralized verification systems that suffer from single points of failure, lack of transparency, and reliance on third-party services by offering a secure and tamper-resistant decentralized proof of existence for digital files.
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Alinsha S, A Althaf, Chris P Reji, Fahad Mohammed A · 6 authors
Electronic voting techniques have gained popularity as a contemporary alternative to traditional paper-based elections because of their effectiveness and accessibility. The current electronic voting methods, however, have significant security flaws, such as multiple voting, identity theft, centralized control, and a lack of transparency. Despite the fact that blockchain technology is decentralized, immutable, and auditable, many blockchainbased voting systems merely employ cryptographic credentials and lack robust voter identification verification processes. The blockchain-based electronic voting system SecureVote, which incorporates multi-factor verification and facial biometric authentication, is proposed in this study. Ethereum smart contracts are used by the system to guarantee transparent result calculation and tamper-proof vote storage. SecureVote employs one-time password (OTP) validation as a secondary authentication method in conjunction with client-side facial recognition and deep learning-based feature extraction. The suggested design makes use of Web3.js and a decentralized application (DApp) concept for safe wallet-based transaction signing and blockchain interaction. High authentication reliability, avoidance of double voting, and effective transaction processing with low gas overhead are all demonstrated by the experimental results. SecureVote combines biometric multifactor authentication with blockchain immutability to enhance the reliability, transparency, and integrity of remote voting.
Open access
2 source records
Internet Traffic Analysis and Secure E-voting
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Dr. A. Radhika, D. Avinash, D. Sowjanya, K. Karthik · 5 authors
The increasing use of digital communication has made it essential to maintain the confidentiality, integrity, and authenticity of sensitive information. Conventional image steganographic methods offer data hiding in digital images, but they fail to offer effective tamper proofing and secure ownership verification. To overcome these issues, this paper presents a Blockchain-Integrated Secure Image Steganography system using IPFS and Ethereum. In the proposed system, secret data is hidden within digital images using a Least Significant Bit (LSB) image steganographic method developed in Python. The stego images are then stored in the Inter Planetary File System (IPFS) for efficient and decentralized data storage. To ensure data integrity and secure access, the cryptographic hash values of the stego images and their corresponding IPFS Content Identifiers (CIDs) are securely stored on the Ethereum blockchain using smart contracts. The use of blockchain technology provides immutability, transparency, and tamper resistance, and IPFS provides decentralized storage without depending on centralized storage servers. The proposed system is validated to offer high image quality with negligible distortion and robust data security and traceability. This system is applicable for secure data sharing in confidential communication, digital forensics, and secure document transfer.
Open access
Advanced Steganography and Watermarking Techniques
Decentralized storage platforms and blockchain systems offer novel opportunities for data exchange; however, they also present significant challenges in safeguarding sensitive visual information. The Interplanetary File System (IPFS) offers efficient distributed storage, but it lacks built-in confidentiality mechanisms, making additional security layers necessary. This work proposes a security-oriented framework that integrates (k,n) threshold visual cryptography (shamir secret ), LSB-based image steganography, and blockchain-based ownership management using non-fungible tokens (NFTs). Sensitive images are divided into multiple visual shares using a threshold scheme so that no useful information can be obtained unless enough shares are available. Each share is then hidden inside a cover image using a simple LSB-based steganography method and stored on IPFS. Instead of storing the data itself on the blockchain, NFTs are used only to reference the stored content and record ownership in an immutable manner. Experimental results are evaluated using common image quality and statistical metrics, including PSNR, SSIM, correlation, and entropy. With PSNR = Inf dB for all images, Entropy analysis shows that the entropy values of the original cover images are approximately 7.0865, while the entropy values of the stego-images after embedding range between 7.0907 and 7.0954, indicating only a slight increase in randomness. This minimal change confirms that the LSB-based steganographic embedding does not significantly alter the statistical properties of the cover images. The findings show that the original images can be reconstructed with acceptable visual quality while preserving the statistical characteristics of the cover images. The proposed approach demonstrates that combining visual cryptography with decentralized storage and blockchain-based ownership can offer improved confidentiality compared to direct on-chain image storage, without introducing excessive system complexity.
Open access
Advanced Steganography and Watermarking Techniques
The digital art industry faces critical challenges in copyright protection and privacy preservation that existing solutions fail to adequately address. Traditional digital watermarking techniques are vulnerable to removal attacks and cannot prevent unauthorized content access, while current Non-Fungible Token (NFT) platforms expose transaction details and artwork content due to blockchain transparency, creating privacy risks for creators and collectors. Conventional encryption methods require decryption before any data processing, making copyright verification and feature extraction impossible in encrypted states, thus creating a fundamental security-usability trade-off. To overcome these limitations, this research proposes a network security protection system integrating homomorphic encryption with NFT copyright protection. Homomorphic encryption was selected because it uniquely enables computational operations on encrypted data without decryption, allowing copyright verification while maintaining complete data confidentiality – a capability unmatched by alternative privacy-preserving technologies. The system employs the Cheon-Kim-Kim-Song (CKKS) homomorphic encryption algorithm to construct a three-tier protection architecture consisting of an encryption layer, verification layer, and storage layer. This architecture achieves copyright verification and feature extraction of digital artworks in ciphertext state by integrating zero-knowledge proof for identity authentication and Shamir’s secret sharing for secure key management. The NFT copyright protection mechanism introduces homomorphic watermark embedding and smart contract verification, combined with proxy re-encryption to implement secure copyright transfer. A prototype system was developed and evaluated through comprehensive testing. Security performance was assessed using six metrics: privacy protection strength, copyright verification accuracy, anti-tampering capability, key security, transaction anonymity, and system resilience. Each metric was scored on a 0–100 scale based on standardized penetration testing and cryptographic attack simulations, with the comprehensive security score calculated as the weighted average of all metrics. Performance testing on 100 digital artworks across five resolutions (256×256 to 4096×4096 pixels) demonstrates that encryption time for 512×512 resolution images is kept within 15 seconds, while security testing reveals the system achieves a comprehensive security score of 94.7, representing a 60.5% improvement over traditional NFT platforms. This solution provides a practical copyright protection framework balancing security and usability for the digital art industry, with significant theoretical value and broad application prospects.
Open access
Advanced Steganography and Watermarking Techniques
ABSTRACT Non‐fungible tokens (NFTs), as blockchain‐based cryptographic assets certifying unique digital ownership, have emerged as a transformative force in brand–consumer interactions. Building on prior work that primarily examined the social value of complementary NFTs, the current research advances the literature by systematically comparing two fundamental NFT typologies: complementary NFTs (interrelated collections with stylistic and attribute variations) and replicative NFTs (characterized by serialized identical or similar units). Through four experiments, this research demonstrates that these NFT types differentially influence core brand relationship constructs—while complementary NFTs prove more effective in cultivating brand intimacy, replicative NFTs significantly enhance perceived brand congruence. Moreover, brand history and brand strength are key boundary conditions moderating these effects. Our findings not only systematically demonstrate the divergent effects of replicative and complementary NFTs on consumer perceptions but also provide actionable guidelines for NFT portfolio strategy based on specific brand relationship objectives.
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
In the digital era, protecting visual content from misuse and forgery is essential. This study proposes a robust image watermarking method by integrating Discrete Wavelet Transform (DWT), Hessenberg Decomposition (HD), and Singular Value Decomposition (SVD), aiming to enhance watermark imperceptibility and resilience against common image attacks. Additionally, the system incorporates RSA digital signatures within the watermark metadata to ensure verifiable authenticity in NFT (Non-Fungible Token) applications. The method was implemented using Python and tested on multiple grayscale images across various attack scenarios, including noise addition and compression. Experimental results demonstrate high SSIM and PSNR values, confirming the method's effectiveness in maintaining both visual fidelity and embedded watermark integrity. These findings support the potential of this approach for secure and scalable NFT copyright protection.
Open access
Advanced Steganography and Watermarking Techniques
Prof. Abhijeet More, Tejashree B. Patil, Deep Kharate, M P Akhil · 5 authors
As the multi-chain digital assets, decentralized finance (DeFi) and non-fungible tokens (NFTs) seeing rapid development, cryptocurrency portfolio management is causing strong pain among users.With the growing number of blockchain networks like Ethereum and a variety of chains, users commonly have assets across multiple wallets, protocols and dApps.Classic portfolio tracking services often require the constant relationship between client and server, with centralized servers, offering heavy privacy issues and security implications.Manual and account based access Many of these systems require data to be manually entered or employees to sign in with their accounts, which opens up the possibility for data leaks, inaccurate reporting, and divulgence of sensitive financial information.More centralized trackers unfortunately have a very poor understanding of more advanced DeFi functions such as staking, joining liquidity pools, and yield farming positions, total or just plain token approval permissions leading to either incomplete or worse yet misleading asset summaries.To solve the above issues, this system suggests a completely decentralized cryptocurrency portfolio tracker on client-side.The code utilizes APIs like Alchemy, Zapper and CoinGecko to read real-time token balances, NFTs creatures or positions (for DeFi), and allowances from the current network directly offchain.Being exclusively client side, the tracker does not rely on centralized databases and it is designed to minimize privacy compromises.The built-in on-chain security module is its most noticeable feature, as it detects any potentially malicious or extremely large token approvals given to smart contracts.Suspicious approvals can be detected, and then revoked in a timely manner through signed wallet transactions without needing to reveal any private keys.The results show that this decentralized tracker would provide significantly better user privacy, data accuracy and overall security.As a serverless applications service, that bypasses central authentication, as well as database storage, it offers a transparency, user-centric and scalable way to manage digital assets securely.
Open access
Internet Traffic Analysis and Secure E-voting
Chaos-based Image/Signal Encryption
Advanced Steganography and Watermarking Techniques