Blockchain Papers

Tiantian Wu, Yixuan Shen, Fan Zhang, You Jiang · 7 authors

No abstract is available for this record.

Dmitry Belousov, Yury Yanovich

Non-fungible tokens (NFTs) have become a significant digital asset class, each uniquely representing virtual entities such as artworks. These tokens are stored in collections within smart contracts and are actively traded across platforms on Ethereum, Bitcoin, and Solana blockchains. The value of NFTs is closely tied to their distinctive characteristics that define rarity, leading to a growing interest in quantifying rarity within both industry and academia. While there are existing rarity meters for assessing NFT rarity, comparing them can be challenging without direct access to the underlying collection data. The Rating over all Rarities (ROAR) benchmark addresses this challenge by providing a standardized framework for evaluating NFT rarity. This paper explores a dimension reduction approach to rarity design, introducing new performance measures and meters, and evaluates them using the ROAR benchmark. Our contributions to the rarity meter design issue include developing an optimal rarity meter design using non-metric weighted multidimensional scaling, introducing Dissimilarity in Trades (DIT) as a performance measure inspired by dimension reduction techniques, and unveiling the non-interpretable rarity meter DIT, which demonstrates superior performance compared to existing methods.

Hasib Ahmed Md Khyrul Islam, Huy T. Vo, Aditya Rane

In the era of synthetic media, deepfake manipulations pose a significant threat to information integrity. To address this challenge, we propose TrustDefender, a two-stage framework comprising (i) a lightweight convolutional neural network (CNN) that detects deepfake imagery in real-time extended reality (XR) streams, and (ii) an integrated succinct zero-knowledge proof (ZKP) protocol that validates detection results without disclosing raw user data. Our design addresses both the computational constraints of XR platforms while adhering to the stringent privacy requirements in sensitive settings. Experimental evaluations on multiple benchmark deepfake datasets demonstrate that TrustDefender achieves 95.3% detection accuracy, coupled with efficient proof generation underpinned by rigorous cryptography, ensuring seamless integration with high-performance artificial intelligence (AI) systems. By fusing advanced computer vision models with provable security mechanisms, our work establishes a foundation for reliable AI in immersive and privacy-sensitive applications.

Jaydeep Taralkar, Swapnil Narlawar

In the age of deepfakes and Artificial Intelligence (AI) generated content, the authenticity of digital media has become an increasingly pressing concern. Deepfake technology, which leverages advanced machine learning algorithms, such as generative adversarial networks (GANs), enables seamless manipulation of video content, leading to the rapid spread of misinformation, erosion of public trust, and significant potential for reputational harm. This growing threat to the integrity of the media requires innovative verification methods that can reliably establish the provenance and authenticity of digital content. This paper investigates the transformative role of blockchainbased Non-Fungible Tokens (NFTs) as a means of ensuring video authenticity. By tokenizing videos and linking them to a verifiable decentralized blockchain ledger, the proposed approach creates an immutable record that details the entire life cycle of a video from creation to any subsequent transfers of ownership. This digital certificate of authenticity not only preserves the original metadata and provenance of the content, but also safeguards against unauthorized alterations and tampering. Furthermore, the paper presents a comprehensive framework for NFT-based video tokenization, providing a practical implementation using Python and Ethereum smart contracts. This implementation demonstrates how blockchain technology can be harnessed to embed secure and tamper-proof ownership data directly into video content, thus establishing a transparent and reliable method for content verification. By integrating robust cryptographic techniques with decentralized ledger systems, the proposed solution addresses the limitations of traditional centralized verification methods, which are often susceptible to hacking and other forms of manipulation. Ultimately, this paper argues that in an era where digital content is under constant threat from sophisticated AI manipulations, adopting blockchain-based NFT tokenization is not merely an innovative technological solution, but a critical requirement for maintaining the integrity of digital media on modern internet platforms.

Abhishek Bhattarai, Abdulhadi Sahin, Maryna Veksler, Ahmet Kurt · 7 authors

As cryptocurrencies have become increasingly used as an alternative to regular cash and credit card payments, the wallet solutions/apps that facilitate their use have also become increasingly popular. This has also intensified the involvement of these crypto wallet apps in criminal activities such as ransom requests, money laundering, and transactions on dark markets. From a digital forensics point of view, it is crucial to have tools and reliable approaches to detect these wallets on devices and extract their artifacts quickly with greater efficiency. However, with current research and trends, forensic investigators still need to manually extract these file artifacts, which delays the time-sensitive investigation findings. As mobile devices increasingly facilitate cryptocurrency transactions, there emerges a critical gap and need for automated evidence extraction to detect crucial artifacts preventing illicit activities. Therefore, in this paper, we present a comprehensive framework that incorporates various machine learning (ML), image processing, and natural language processing (NLP) approaches to enable fast and automated extraction/triage of crypto-related artifacts from Android and iOS devices. Specifically, our method can automatically detect which crypto wallet exists on the device, their artifacts (i.e., database/log files), along with the crypto-related images, web browsing data, and SMS conversations. For each type of data, we offer a specific ML technique, such as Support Vector Machine, Logistic Regression, and Neural Networks, to detect and classify these files. Our evaluation results show very high accuracy compared to alternative tools: our wallet classification model achieves 91% recall, crypto-related image classification achieves 75% accuracy, browsing data achieves 100% accuracy, and the SMS message model achieves 85% accuracy.

Wang Zhujun, Zhang Lejun, Xueqing Li, Tian Zhihong · 10 authors

Blockchain, as a distributed ledger, inherently possesses tamper-resistant capabilities, creating a natural channel for covert communication. However, the immutable nature of data storage might introduce challenges to communication security. This study introduces a blockchain-based covert communication model utilizing dynamic Base-K encoding. The proposed encoding scheme utilizes the input address sequence to determine K to encode the secret message and determines the order of transactions based on K, thus ensuring effective concealment of the message. The dynamic encoding parameters enhance flexibility and address issues related to identical transaction amounts for the same secret message. Experimental results demonstrate that the proposed method maintains smooth communication and low susceptibility to tampering, achieving commendable concealment and embedding rates.

Abdullah Ayub Khan, Asif Ali Laghari, Syed Azeem Inam, Sajid Ullah · 6 authors

Rapid technological breakthroughs in recent years, like Deepfake, have made it feasible to produce synthetic media that is remarkably lifelike, but they also present significant hazards to public trust, privacy, and security. This survey paper reviews the latest techniques for detecting deepfakes, focussing on important components as image and video manipulation, audio spoofing, and multimodal synthesis. It features state-of-the-art methods including machine learning (ML), deep learning (DL), and multimodal architectures that are especially made to address the previously described deepfake criteria. The report provides a critical review of assessment measures used to assess detection model performance, including precision, accuracy, recall, computing effectiveness and efficiency, and fast responses to adversarial attacks. In order to assist direct future research, this highlights recent advancements in the subject, including explainable AI, federated learning, and self-supervised learning hierarchy. In order to examine the problems with adversarial attacks, scalability across different datasets, and the ethical implications of detection techniques, it is also vital to look into the technological and societal challenges surrounding multimedia-enabled deepfake detection. In particular, the usage of Blockchain Distributed Ledger Technology (BDLT) for traceability, lightweight modelling, and resilient systems forms for cross-model deepfake evaluation are discussed in this review study along with potential solutions to these limitations and areas for further research. This paper offers a comprehensive resource for future research, experts, and practitioners looking to combat the growing threat of deepfake, especially in the social media space, using innovative and useful detection tools.

G. Rekha, T. Sudha

Recent investigative frame works, digital forensic tools, and techniques are incapable of acquiring the IoT paradigm's dispersion and heterogeneity characteristics that make law enforcement organizations and digital forensic investigators do their tasks. To address these issues, this study presents a Blockchain-assisted digital forensic system for the IoT context. The devices in the perception layer contain several forensic evidences, which are transmitted to the further layer called the Fog layer. Moreover, the secure transmission of messages from the perception layer to the fog layer is a major challenge. To address this issue, the user's identity (investigators and devices) can be mapped to a pseudo-identity and the security of signature and confidentiality mechanisms ensures both privacy and security. Subsequently, the Fog layer verifies the signature data and employs an improved Blowfish encryption algorithm to provide security to the evidence transmission. In order to encrypt the evidence, the optimal key is generated using the SSAJO algorithm. Then the encrypted message is subjected to a distributed ledger called consortium blockchain that improves the security factors with a greater level of control. Moreover, the proposed IoT framework controls the cloud locally to store, and access data, and performs occasional synchronization with a consortium blockchain.

Atsuki Koyama, Kentaroh Toyoda, Manato Fujimoto, Thi Hong Tran

The rapid advancement of deepfake technology poses serious risks, including financial fraud and political misinformation, demanding robust methods for verifying image content authenticity. While the C2PA standard and zero-knowledgeproof-based methods provide an image content authenticity proving mechanism, the existing solutions struggle to efficiently support privacy-preserving edits and iterative modifications. To address these challenges, we propose zk-REAL (Zero-Knowledge-Based Protocol for Repeated Image Edit Authenticity Proof with Lattice Hashing), a framework that leverages a lightweight lattice-based hashing scheme within a zero-knowledge proof system. Our approach significantly reduces computational overhead, enabling faster proof generation and smaller proof size even for high-resolution images. Additionally, the updatability of our hashing method supports iterative edits, such as mosaicking or partial modifications, by minimizing redundant computations. Finally, to ensure compatibility with the C2PA ecosystem and conventional signature verifications, we integrate SHA-256 outside of the zero-knowledge circuit. Our evaluation shows up to a 29% reduction in computational costs for proof generation, showcasing the potential of zk-REAL in practical content authenticity verification scenarios.

Stefan Dziembowski, Shahriar Ebrahimi, Parisa Hassanizadeh

Ensuring the authenticity and credibility of daily media on internet is an ongoing problem. Meanwhile, genuinely captured images often require refinements before publication. Zero-knowledge proofs (ZKPs) offer a solution by verifying edited image without disclosing the original source. However, ZKPs typically come with high costs, particularly in terms of prover complexity and proof size. This paper presents VIMz, a framework for efficiently proving the authenticity of high-resolution images using folding-based zkSNARKs; a type of proving system that minimizes computational overhead by recursively folding multiple evaluations of the same constraints into a compact proof. As a complete proof system, VIMz proves the integrity of both the original and edited images, as well as the correctness of the transformation without revealing intermediate images within a chain of edits--only the final result is disclosed. Moreover, VIMz maintains the anonymity of the original signer and all subsequent editors while proving the authenticity of the final image. We also compare VIMz with the system model in Coalition for Content Provenance and Authenticity (C2PA) from different perspectives and show that VIMz offers higher level of security guarantee by eliminating the need to trust the editing environment. Experimental results show that VIMz performs efficiently in both prover and verifier sides. It can prove the transformations on 8K (33MP,i.e., 100MB) images with up to 13%~25% faster than the competition, while reaching to a peak memory of only 10 GB. Moreover, VIMz has a verification time of under 1 second and achieves succinct proofs of less than 11 KB for all resolutions, which is more than 90% improvement compared to the competition. VIMz's low memory complexity allows for proving multiple transformations in parallel to achieve a 3.5x additional speedup on average.

Zhuo Chen, Liehuang Zhu, Peng Jiang, Jialing He · 5 authors

Blockchain-based covert communication enables undetectable data transmission by constructing covert channels in blockchain networks. However, extant approaches require transactions carrying secret data to be permanently preserved in the public ledger, which cannot resist data mining. Besides, these solutions cost up to $48,169 to transmit 1-MegaByte (MB) data. In this paper, we introduce a Tripartite Covert Communication Model (TCCM), which amalgamates blockchain and the Inter Planetary File System (IPFS) to facilitate the transfer of MB-level files while simultaneously circumventing data mining. TCCM comprises an IPFS covert channel, a ledger-layer covert channel, and a network-layer covert channel. The IPFS covert channel transmits the initial secret data. The ledger-layer covert channel embeds a timestamp into the blockchain transaction, which governs the construction time of the network-layer covert channel. The network-layer covert channel conveys the content identifier of the secret data utilizing Bitcoin's inventory message. We further present a TCCM instantiation and formally prove its unobservability. We instant TCCM to evaluate its performance on the Bitcoin mainnet. We also discuss its scalability, real-world use cases, and ethical considerations. Experimental outcomes demonstrate that the proposed instantiation is unobservable and able to transmit 100-MB files at a cost of $1.47.

Alanoud M. Almhlbdi, Norah D. Altowairqi, Areej Alshutayri, Rehab Qarout

Identifying hidden payloads in images has become increasingly critical as steganography continues to challenge traditional security measures. This paper introduces a deep learning framework for both the detection (binary classification) and fine-grained classification (multi-class) of steganographic payloads embedded using Least Significant Bit (LSB) techniques. The proposed system distinguishes between benign images and stego images containing five different payload types: HTML, JavaScript, PowerShell, URLs, and Ethereum-related data. To achieve this, we systematically evaluate various architectures, including a custom Convolutional Neural Network (CNN), hybrid CNN-GRU and CNN-LSTM models, and a Vision Transformer (ViT) at different input resolutions using 5-fold cross-validation. Our experiments reveal a critical finding: image resizing significantly degrades detection performance, as subtle LSB artifacts are often corrupted. While our custom CNN model achieved the highest mean cross-validation accuracy (0.9702), the hybrid CNN-GRU model demonstrated superior generalization on the held-out test set and external dataset, achieving a multi-class accuracy of 0.98 on the testset and 0.97 on the external. This result highlights the advantage of combining the CNN’s spatial feature extraction with the GRU’s ability to model sequential dependencies for robust payload identification on unseen data.

S.B. Gurumurthy, Deepa Yogish

No abstract is available for this record.

Adam Costello, R. V. Gundur

No abstract is available for this record.

Devaseelan S, B. Praveen

The proposed work builds upon the Random Forest machine learning algorithm to improve the process of digit forensic investigation in case of NFT. The following structure of this framework is aimed at identifying and disabling fraudulent or suspicious activities in NFT transactions by comparing different parameters like the Detection Time, False Positive Rate, the Total Transaction Volume Analyzed, the Anomalous Transaction Ratio, Clustering Accuracy, Data Utilization Efficiency, and Detection Sensitivity. Through using Random Forest, a solid ensemble learning technique that is well known for its on high accuracy as well as off overfitting tendency, it optimistically improves the identifying abilities of the framework in isolation of the false positives. The ability of the proposed system to deliver optimal results is further explained by line plots, area charts, histograms, and stem plots which all provide the variation of these metrics as the time proceeds. Not only does it enhance the effectiveness of detecting the fraudulent transactions, but it also enhances the application of data in the forensic analysis that creates a great advantage in the increasing realm of digital assets for investigators.

Michael Prummer, Emanuel Regnath, Saurabh Singh, Harald Kosch

The increasing digitalization in manufacturing and trends like the Industrial Metaverse drive the need for secure decentralized asset exchanges and industrial asset intellectual property (IP) rights protection. Utilizing distributed ledgers and non-fungible tokens (NFTs) linked by a hash enables the management and authentication of assets. However, challenges arise in authenticating the same asset’s different versions and file formats using a single hash.In this work, we propose a new NFT structure that allows binding multiple files existing in different file formats, representing the same digital asset by combining a Merkle Tree (MT) with a calculated Average Perceptual Hash (APH). While the MT proves bitwise integrity, the APH verifies functional integrity for asset authentication. We evaluate our approach for exchanging Printed Circuit Board (PCB) designs in a decentralized ecosystem by calculating unique PCB fingerprints and the APH. Our results show a robust identification and integrity verification of PCB designs depending on the manipulation type. Our approach for asset authentication is generalizable to all asset classes with an appropriate perceptual hash.

Anurag Pandey, Jayanti Rout, Ashutosh Soni, Surendra Kumar Nanda

In the era of digital technology, the widespread availability of multimedia content has presented major challenges in confirming the genuineness and reliability of digital media. This paper introduces a novel technology that utilizes the InterPlanetary File System (IPFS) and Ethereum to authenticate multimedia content, ensuring its provenance and prohibiting tampering. The suggested approach brings together a decentralized ledger system with cryptographic hashing and smart contracts to create an immutable record of content ownership and modification history. By integrating cryptographic hashes of multimedia files into blockchain transactions, we establish a secure, transparent, and tamper-proof mechanism for tracking content authenticity. Our methodology improves confidence and dependability in digital media by establishing a strong structure for creators, distributors, and consumers to authenticate the authenticity of multimedia materials. Experimental results confirm the effectiveness and efficiency of our system in preserving the integrity of various types of multimedia content, laying the path for a wider utilization areas such as digital rights management, content distribution, and forensic analysis, etc.

Guodong Ye, Zhuozhao Chen

No abstract is available for this record.

Blerim Krasniqi, Eliza Stefanova

The emergence of Web3 technologies presents new challenges and implications for investigative techniques and evidence acquisition in the field of digital forensics. This research paper delves into the intricate landscape of Web3 and its impact on the investigative processes involved in digital forensics. By examining the unique characteristics of Web3, such as decentralized networks, blockchain technology, and smart contracts, this study aims to highlight the complexities that digital forensic investigators face in this rapidly evolving environment.Through a comprehensive review of existing literature, this paper identifies key challenges in conducting digital investigations within Web3 ecosystems. These challenges include issues related to data integrity, privacy concerns, jurisdictional boundaries, and the authentication of digital evidence. Furthermore, the implications of these challenges on traditional investigative techniques are discussed, emphasizing the need for adaptation and innovation in digital forensic practices.Overall, this research sheds light on the evolving nature of digital forensics in the context of Web3 technologies, providing insights for forensic practitioners, law enforcement agencies, and policymakers to better navigate and address the complexities of investigating digital crimes in decentralized and blockchain-based environments.

Deepesh Chaudhari, Sandeep K. Shukla

As the adoption of cryptocurrencies continues to grow, so does the complexity and scale of financial crimes involving digital assets. The need for a robust crypto crime investigation tool has never been more critical. Blockstash Intelligence is an advanced solution designed to meet this demand by equipping law enforcement agencies, financial institutions, and compliance teams with the tools necessary to track, analyse, and combat illicit activities in the crypto space. Our platform supports multiple blockchains, including Bitcoin (BTC), Ethereum (ETH), and Tron(TRX), offering a wide-ranging capability to investigate across different cryptocurrency ecosystems. Blockstash Intelligence provides a suite of features such as graph visualization of crypto transactions, real-time transaction monitoring, and compliance capabilities. With real-time off-chain to on-chain data mapping, path generation from wallets to exchanges, and comprehensive wallet assessments, Blockstash Intelligence is a helpful resource for tracing and investigating cryptocurrency-related crimes.

Saurabh Nandwani, David Ostrowski

The proliferation of deep fake technology has raised significant concerns regarding the authenticity and integrity of digital media. This paper proposes a novel approach that leverages Non-Fungible Token (NFT) technology within blockchain combined with generative AI to detect and authenticate digital media, addressing the growing issue of deep fakes. Using NFTs, unique digital certificates of authenticity are created for each media piece and securely stored on a blockchain to ensure immutability and traceability. Generative AI models, specifically designed for anomaly detection, are employed to analyze media files and identify potential manipulations by comparing them against the original authenticated versions. This dual-layered system not only enhances the reliability of media verification but also provides a robust framework for ensuring the security and provenance of digital content. The implementation of this system demonstrates a significant improvement in detecting deep fakes, offering a practical solution to mitigate the risks associated with synthetic media. Intermediate results highlight the potential of integrating blockchain and AI technologies to establish a more secure and trustworthy digital ecosystem.

Etienne Levecque, Jan Butora, Patrick Bas

Given a JPEG pipeline (compression or decompression), this paper demonstrates how to find the antecedent of an 8x8 block. If it exists, the block is considered compatible with the pipeline. For unaltered images, all blocks remain compatible with the original pipeline; however, for manipulated images, this is not necessarily true. This article provides a first demonstration of the potential of compatibility-based approaches for JPEG image forensics. It introduces a method to address the key challenge of finding a block antecedent in a high-dimensional space, relying on a local search algorithm with restrictions on the search space. We show that inpainting, copy-move, and splicing, when applied after JPEG compression, result in three distinct mismatch problems that can be detected. In particular, if the image is re-compressed after modification, the manipulation can be detected when the quality factor of the second compression is higher than that of the first. Through extensive experiments, we highlight the potential of this compatibility attack under varying degrees of assumptions. While our approach shows promising results-outperforming three state-of-the-art deep learning models in an idealized setting-it remains a proof of concept rather than an off-the-shelf forensic tool. Notably, with a perfect knowledge of the JPEG pipeline, our method guarantees zero false alarms in block-by-block localization, given sufficient computational power.

Sandip Shinde, Sankalp Nilesh Chordia

The expansion of decentralized cryptocurrencies poses notable complexities for law enforcement in terms of detecting unlawful behaviors, as well as in the identification of individuals and the retrieval of transaction histories of perpetrators who take advantage of the pseudonymous nature inherent in the cryptocurrency system. This research paper puts forth a solution called Kavach. The designated user of Kavach will be an investigator. The tool leverages graph machine learning for the categorization of transactions as illicit or legitimate. It utilizes graph-based embeddings for the recognition of potentially suspicious addresses within the bitcoin network. The tool incorporates a predefined watch list for such suspicious addresses and will have the capability to trace the digital trail of these addresses using Open Source Intelligence (OSINT).

Remzi Gürfidan

Abstract In this study, we propose an effective system called RG-Guard that detects potential risks and threats in the use of cryptocurrencies in the metaverse ecosystem. In order for the RG-Guard engine to detect suspicious transactions, Ethereum network transaction information and phishing wallet addresses were collected, and a unique dataset was created after the data preprocessing process. During the data preprocessing process, we manually distinguished the features within the original dataset that contained potential risk indicators. The learning process of the RG-Guard engine in risk classification was achieved by developing a deep learning model based on LSTM + Softmax. In the training process of the model, RG-Guard was optimised for maximum accuracy, and optimum hyperparameters were obtained. The reliability and dataset performance of the preferred LSTM + Softmax model were verified by comparing it with algorithms used in risk classification and detection applications in the literature (Decision tree, XG boost, Random forest and light gradient boosting machine). Accordingly, among the trained models, LSTM + Softmax has the highest accuracy with an F 1-score of 0.9950. When a cryptocurrency transaction occurs, RG-Guard extracts the feature vectors of the transaction and assigns a risk level between 1 and 5 to the parameter named β risk . Since transactions with β risk > = 3 are labelled as suspicious transactions, RG-Guard blocks this transaction. Thus, thanks to the use of the RG-Guard engine in metaverse applications, it is aimed to easily distinguish potential suspicious transactions from instant transactions. As a result, it is aimed to detect and prevent instant potential suspicious transactions with the RG-Guard engine in money transfers, which have the greatest risk in cryptocurrency transactions and are the target of fraud. The original dataset prepared in the proposed study and the hybrid LSTM + Softmax model developed specifically for the model are expected to contribute to the development of such studies.