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).
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.
Cryptocurrency markets are often characterized by market manipulation or, at the very least, by a sharp distinction between large and sophisticated investors and small retail investors.While traditional assets often see a divergence in the success of institutional traders and retail traders, we find an even more pronounced difference regarding the holders of Ethereum (ETH), the secondlargest cryptocurrency by volume.We see a significant difference in how large holders of ETH behave compared with smaller holders of ETH relative to price movements and the volatility of the cryptocurrency.We find that large ETH holders tend to increase their ETH holdings prior to a price increase, while small ETH holders tend to reduce their ETH holdings prior to a price increase.In other words, ETH returns tend to move in the direction that benefits crypto "whales" while reducing returns (or increasing loss) to "minnows."Additionally, we find that the volatility of ETH returns seems to be driven by small retail investors rather than by the crypto whales.Despite the advantages that DeFi (decentralized finance) trading networks offer, our findings provide evidence that many of the same challenges and vulnerabilities of TradFi (traditional finance) seem to remain within the ETH ecosystem, where larger, more sophisticated investors reap the benefits of their comparative advantages.
The domain of Digital Forensics for the Industrial Internet of Things (IIoT) and the proposed use of a Distributed Digital Ledger (DDL), has for the most part been theoretical in nature within the current literature. The work in this paper explores the practical feasibility of using DDL technology for Digital Forensics in the IIOT context. We detail a new methodology for testing the performance of writing to and reading from a DDL in an IIOT environment, and present findings on the overhead associated with storing and retrieving IIoT transactions in a DDL. We conclude that while it is possible to build and use a DDL for storing IIoT transactions, there are limitations to the number of sensors that can be supported by a single implementation and the time it takes to retrieve transactions may be too high to be practical for Digital Forensics.
Dmitry Belousov, Maksim Shuklin, Alexander Stepin, Yury Yanovich
Rarity meters are incorporated by industry and discursive by academia. Rarity, as an intuitive term, attracted numerous researchers to present their own view of it. While there is existing literature on comparing rarity meters, it requires access to NFT collection data, which can be challenging for researchers without a background in blockchain technology. This has created a demand for an easily accessible rarity meter benchmark. In this paper, we introduce the Rating over all Rarities (ROAR) benchmark, which includes data from one hundred popular NFT collections from the Ethereum blockchain, implemented a weighted correlation-based performance measurement function, as well as four state-of-the-art rarity meters (Rarity.tools, Kramer, OpenRarity, and NFTGo), along with a new rarity meter called ROAR. Our experiments show that the ROAR rarity meter, an ensemble of the other four meters, outperforms its competitors, with Rarity.tools and Kramer as runner-ups. The ROAR benchmark is a tool for examination and testing of rarity meter ideas, and we challenge readers to develop models that can outperform the ROAR rarity meter.
Arad Kotzer, Mostafa Naamneh, Ori Rottenstreich, Pedro Reviriego
Non-fungible tokens (NFTs) are digital assets representing ownership or proof of authenticity of a unique item. NFTs are blockchain-based and rely on smart contracts. The increase in duplicate NFTs in recent years brings the need for discovery tools for forged NFTs, some of which include using image hash functions. Though the problem of image duplication is widely discussed, detecting NFT duplications requires using fast detection methods as a new NFT image needs to be compared with the entire NFT history on the blockchain. In this paper, we analyze the performance of several image-hash functions, examine the cases where each function performs well, and evaluate multiple image-hash-functions-based NFT duplication detectors. Our approach achieves high accuracy in detecting NFT duplications and demonstrates that using several hash functions rather than one increases the ability to detect duplications.
Digital Media Forensic Detection
Advanced Steganography and Watermarking Techniques
Mohamed El Amine Kheraifia, Abdelatif Sahraoui, Makhlouf Derdour
The video surveillance system is a key component of the technologies deployed in smart cities. It serves a variety of applications, including public safety, crime prevention, traffic management, and environmental monitoring. The data captured by these systems includes sensitive information related to privacy, crime and national security, requiring robust protection against data breaches to ensure confidentiality. In this paper, we introduce a video fingerprinting-based method that uses a timestamp and device number, intended to prevent and detect image manipulation or replacement of original images with copies during transmission and of receiving the monitored data. Additionally, we propose the use of a blockchain system with an immutable distributed ledger for traceability and auditing of authentication procedures.
Advanced Steganography and Watermarking Techniques
The rapid proliferation of AI-generated “deepfake” images, audio, and video is eroding public trust in digital media and amplifying risks to elections, markets, journalism, and personal safety. While AI detection models have improved, they face an adversarial “cat-and-mouse” problem and often struggle to generalize across manipulation methods and compression regimes. This manuscript proposes and analyzes a hybrid, end-to-end approach that couples upstream provenance and authenticity signals—anchored via open standards (e.g., C2PA Content Credentials) and decentralized ledgers—with downstream AI detection and moderation. The pipeline captures and signs media at source; binds verifiable, tamper-evident metadata; anchors cryptographic hashes on a public or consortium blockchain; stores originals off-chain with content addressing (e.g., IPFS/Filecoin); and fuses these trust signals with model-based detectors and policy engines at distribution edges. We situate the proposal within current regulation (e.g., EU AI Act transparency duties) and state-of-the-art methods (e.g., watermarking such as SynthID, Stable Signature, and Tree-Ring; deepfake detectors trained on DFDC and FaceForensics++), highlighting both strengths and known attack vectors against watermarking that motivate layered defenses. A simulation-based evaluation illustrates that combining provenance signals with video-level transformer detectors can raise F1 from 0.85 to 0.92 while cutting false positives by ~41% in a balanced test set, primarily by rejecting credential-mismatched or hash-divergent media before expensive model inference. We further discuss privacy-preserving verification using W3C Verifiable Credentials (VC 2.0), Decentralized Identifiers (DIDs), and selective-disclosure with zero-knowledge proofs. The findings make a practical case for “trust by design” built on open standards, decentralized integrity proofs, and robust AI detection, implemented as a policy-aware defense-in-depth stack for platforms and newsrooms.
This research paper proposes a novel approach of integrating traditional steganography techniques with modern deep learning methodology as a solution against prevailing cyber fraud in the blockchain space. Despite standard authentication measures in place, Non-Fungible Tokens (NFTs) are prone to data theft, replication, and misrepresentation of ownership. Hence, our work leverages deep learning infused with advanced crypto-steganography to add multi-layered authentication to these digital tokens. The research elaborates on multiple utilities relevant to NFTs such as their generation, verification, and price forecasting, while emphasizing mainly on authentication to provide a secure ecosystem. The research aims to delve into multiple methodologies that are integrated into a robust, comprehensive and a user-friendly NFT minting platform that works cohesively with the blockchain network. Hence, an extensive study is undertaken on the applications of deep learning in countering data theft in the NFT marketplaces.
Advanced Steganography and Watermarking Techniques
Digital Media Forensic Detection
Generative Adversarial Networks and Image Synthesis
Arash Heidari, Nima Jafari Navimipour, Hasan Dağ, Samira Talebi · 5 authors
Abstract In recent years, the proliferation of deep learning (DL) techniques has given rise to a significant challenge in the form of deepfake videos, posing a grave threat to the authenticity of media content. With the rapid advancement of DL technology, the creation of convincingly realistic deepfake videos has become increasingly prevalent, raising serious concerns about the potential misuse of such content. Deepfakes have the potential to undermine trust in visual media, with implications for fields as diverse as journalism, entertainment, and security. This study presents an innovative solution by harnessing blockchain-based federated learning (FL) to address this issue, focusing on preserving data source anonymity. The approach combines the strengths of SegCaps and convolutional neural network (CNN) methods for improved image feature extraction, followed by capsule network (CN) training to enhance generalization. A novel data normalization technique is introduced to tackle data heterogeneity stemming from diverse global data sources. Moreover, transfer learning (TL) and preprocessing methods are deployed to elevate DL performance. These efforts culminate in collaborative global model training zfacilitated by blockchain and FL while maintaining the utmost confidentiality of data sources. The effectiveness of our methodology is rigorously tested and validated through extensive experiments. These experiments reveal a substantial improvement in accuracy, with an impressive average increase of 6.6% compared to six benchmark models. Furthermore, our approach demonstrates a 5.1% enhancement in the area under the curve (AUC) metric, underscoring its ability to outperform existing detection methods. These results substantiate the effectiveness of our proposed solution in countering the proliferation of deepfake content. In conclusion, our innovative approach represents a promising avenue for advancing deepfake detection. By leveraging existing data resources and the power of FL and blockchain technology, we address a critical need for media authenticity and security. As the threat of deepfake videos continues to grow, our comprehensive solution provides an effective means to protect the integrity and trustworthiness of visual media, with far-reaching implications for both industry and society. This work stands as a significant step toward countering the deepfake menace and preserving the authenticity of visual content in a rapidly evolving digital landscape.
Open access
Digital Media Forensic Detection
Generative Adversarial Networks and Image Synthesis
Advanced Steganography and Watermarking Techniques
Recent years have witnessed the booming growth of 5G and 6G technology, which has brought unprecedented massive data transmission, causing severe privacy issues. However, traditional information encryption and multimedia covert communication fail to protect the identities of communication parties and the originality of messages. The emergence of blockchain provides a promising solution to solve these problems. Its anonymity manages to hide the identities of communication parties, and immutability ensures the message is undestroyable. However, the existing blockchain-based covert communication schemes suffer the issues of low embedding capacity and high time cost. In this paper, an amount-based covert communication scheme over the blockchain is proposed, in which a unique coding method is devised for hiding messages into transaction amounts to improve the embedding capacity. Compared with existing address-based methods, the proposed scheme can apply any address and reduce the time of obtaining special addresses. Besides, we innovate the way to prove the concealment by calculating the relative entropy of the transaction amount between Bitcoin and the proposed scheme. The security of our method is demonstrated by comparing the probability of attackers acquiring secret messages under different adversary capabilities. The experimental results verify that the proposed approach outperforms the existing schemes regarding embedding capacity, time costs, number of transactions, concealment, and security.
Advanced Steganography and Watermarking Techniques
Pei Zhang, Qingfeng Cheng, Mingliang Zhang, Xiangyang Luo
Covert communication is a crucial technology that hides information in the redundant structure of the file and transmission through public channel to achieve the secure delivery of information. The blockchain network, with the characteristics of anonymity, decentralization and tamper-proofing, can make up for the shortcomings of multimedia-based covert communication, which include the easy exposure of the identity for both parties, the vulnerability to destruction during communication and the weak robustness of the channel. Therefore, the blockchain network is an ideal channel for covert communication. Nevertheless, the existing covert communication methods face certain challenges based on blockchain, such as the lack of a secure channel for transferring the master key, low embedding capacity, and weak detection resistance. In view of this, this paper proposes a covert communication method based on Shamir threshold and STC mapping, which is suitable for public chain networks. The proposed method first decomposes the master key into sub-keys by introducing Shamir scheme, and the sub-keys are shared with the help of transaction amounts on a blockchain. Then, a mapping relation is established to ensure that the transaction amounts carrying the secret are evenly distributed. Finally, secret information is hidden in the mapping relationship and the transaction amount is interwoven, which is published to the blockchain through transactions to complete covert communication. The introduction of Shamir threshold breaks the limitation that master key cannot be safely transmitted due to the lack of a secure channel in the research of covert communication based on blockchain, thereby enhances the security of the method. Meanwhile, Shamir threshold scheme based on public chain, can solve the issue that the master key cannot be reconstructed due to dishonest participants providing invalid subkeys on traditional network. In addition, the proposed STC mapping can not only improve the detection resistance but also increase the embedding capacity. A series of experimental results illustrate that the proposed method is more resistant to detection, and the embedding efficiency is enhanced by 27.56 times compared with existing public chain-based covert communication methods, effectively reducing the number of transactions and saving resource consumption.
Advanced Steganography and Watermarking Techniques
Covert channels in blockchain networks achieve undetectable and reliable communication, while transactions incorporating secret data are perpetually stored on the chain, thereby leaving the secret data continuously susceptible to extraction. MTMM (IEEE Transactions on Computers 2023) is a state-of-the-art blockchain-based covert channel. It utilizes Bitcoin network traffic that will not be recorded on the chain to embed data, thus mitigating the above issues. However, we identify a distinctive pattern in MTMM, based on which we propose a comparison attack to accurately detect MTMM traffic. To defend against the attack, we present an improvement named ORIM, which exploits the permutation of transaction hashes within inventory messages to transmit secret data. ORIM leverages a pseudo-random function to obscure the transaction hashes involved in the permutation to ensure unobservability. The obfuscated values, rather than the original transaction hashes, are utilized to encode the confidential data. Furthermore, we introduce a variable-length encoding scheme predicated on complete binary trees. This scheme considerably amplifies the bandwidth and facilitates efficient encoding and decoding of secret data. Experimental results indicate that ORIM maintains unobservability and that ORIM’s bandwidth is approximately$3.7\times $of MTMM.
Advanced Steganography and Watermarking Techniques
Muhammad Iqbal, Arshad Iqbal, Abdullah Alshammari, Ihtisham Ali · 6 authors
Decentralized cryptocurrencies like Bitcoin are digital assets with a price volatility nature, that allow for blockchain-based, peer-to-peer monetary transactions. Due to the price volatility problem with decentralized cryptocurrencies, research into the underlying pricing mechanism is required. Additionally, the behavior of Bitcoin prices is non-stationary, meaning that the statistical distribution of data varies over time. The proposed framework demonstrates the use of sophisticated machine learning models in predicting the short and medium-term trends and actual values of Bitcoin prices. This research goes beyond previous work that has only looked at machine learning-based categorization for a single day by instead using such models to forecast price changes seven, thirty, and ninety days into the future. The generated models are useful and work admirably, with the classification models reaching a maximum accuracy enhancement up to 31.48% for a 90-day prediction and a 11.76% F1-score for a forecast extending to the thirtieth day. In the case of regression the margin of error shifts from the one-time horizon for price projections to the next. A significant notable downfall occurs in different error metrics. These findings suggest that the models given here outperform those already found in the literature.
Open access
Digital Media Forensic Detection
Advanced Steganography and Watermarking Techniques
Zheyi Zhang, Yinghong Cao, Hadi Jahanshahi, Jun Mou
With the rapid development of blockchain technology, the security of non-fungible tokens (NFT) in the transaction process has attracted much attention. In the transaction process, the commoditized NFT images will inevitably involve display and leakage problems, which is likely to lead to economic losses drink copyright disputes between the trading parties. To protect transaction security, a chaotic color multi-image compression encryption/LSB data-type steganography scheme is proposed in the paper. A series of chaotic sequences are obtained by iterating the chaotic map, while compression sensing (CS) is introduced to compress multiple secret images and fuse the compressed secret images into one large secret image. Then this image is encrypted, and the encrypted large secret image is hidden on multiple cover images by steganography. This encryption can hide the change in the statistical properties caused by steganography. Finally, the cover image is combined with the 3D object model by using it as a texture for the 3D object model to realize the type steganography of the image data. The simulation and performance test results of the scheme illustrate that the scheme has a large enough key space and steganography capacity, as well as good resistance to differential attacks, statistical attacks, compression reconstruction quality, and robustness. This scheme can well protect the transaction security of NFT images, and at the same time open a channel connecting 2D images and 3D obj textured models, which provides a new idea for steganography.
Open access
Advanced Steganography and Watermarking Techniques
Inspections into cybercrime rely heavily on the use of digital evidence because of its ability to connect individuals to specific illegal activity. During a probe into a computer crime, it is essential that the integrity, authenticity, and auditability of digital evidence be maintained at all times when it is being transferred through the chain of custody from the beginning to the finish. The digitalization of banking is paralleled by an equally digitalization of the environment for financial crime. Because laws, rules, and forensic techniques are unable to keep up with the fast development of new technologies, investigations into embezzlement schemes might benefit from the standardization of processes and recording of the related approach. The applicability and adaptability of our method may be extended to include a wide variety of fraud investigations as well as routine internal audits. We offer a working Ethereum-based solution, and we incorporate standardised forensic processes and chain of custody preservation techniques. In conclusion, we investigate the challenges surrounding the mutually beneficial link between blockchain technology and financial investigations, as well as the managerial effect and potential avenues for further study. r wicked actors. In this sense, the characteristics afforded by blockchain technology, such as immutability, verifiability, and authentication, contribute to an increase in the degree of rigor that may be achieved in financial forensics. In this article, we describe not only the current status of blockchain-based digital forensic procedures but also a taxonomy of the most popular methodologies used in financial investigations. Our solution makes it possible for consumers to trace the history of their data by making use of smart contracts (CS). In conclusion, the development of an Artificial Neural Network (ANN) for blockchain makes the collection of evidence more easier. Java, which is used for clouds and blockchains, and network simulator-3.26, which is used for software-defined networking (SDN), are both used inside a single testing environment. Response time, Evidence input time, Evidence verification time, All aspects of the suggested forensic architecture, including communication overhead, hash calculation time, key generation time, encryption time, decryption time, and overall change rate, show potential.
Kar Balan, Andrew Gilbert, Alexander Black, Simon Jenni · 6 authors
We present DECORAIT; a decentralized registry through which content creators may assert their right to opt in or out of AI training as well as receive reward for their contributions. Generative AI (GenAI) enables images to be synthesized using AI models trained on vast amounts of data scraped from public sources. Model and content creators who may wish to share their work openly without sanctioning its use for training are thus presented with a data governance challenge. Further, establishing the provenance of GenAI training data is important to creatives to ensure fair recognition and reward for their such use. We report a prototype of DECORAIT, which explores hierarchical clustering and a combination of on/off-chain storage to create a scalable decentralized registry to trace the provenance of GenAI training data in order to determine training consent and reward creatives who contribute that data. DECORAIT combines distributed ledger technology (DLT) with visual fingerprinting, leveraging the emerging C2PA (Coalition for Content Provenance and Authenticity) standard to create a secure, open registry through which creatives may express consent and data ownership for GenAI.
Digital forensics, a crucial subset of cybersecurity, encompasses sophisticated tools and methodologies for the interpretation, analysis, and investigation of digital evidence, facilitating the identification and mitigation of cybercrimes and security breaches. With the advent of cryptocurrencies, an array of unique challenges has emerged in the domain of digital forensic investigations. This review elucidates the prevailing state of digital forensic practices vis-à-vis cryptocurrencies, emphasizing the obstacles and limitations inherent in probing decentralized and intricate technologies. Notable deficiencies in extant investigative practices were observed. Solutions proffered encompass the formulation of novel software applications tailored for cryptocurrency analyses, the integration of machine learning and artificial intelligence capabilities, and the employment of advanced analytics to discern patterns and irregularities within blockchain transactions. Furthermore, a pioneering methodology, merging traditional digital forensic strategies with blockchain-specific techniques, is posited for efficacious cryptocurrency inquiries. The analysis underscores the imperative for a renewed paradigm in digital forensic examinations to surmount the challenges integral to cryptocurrency probes. By forging novel methodologies and standardizing investigative procedures, support for legal enforcement endeavors can be enhanced, facilitating the efficacious detection and prosecution of cryptocurrency-associated misdemeanors.
Blockchain technology has brought notable advancements to diverse industries. The introduction of non-fungible tokens (NFTs) has particularly led to a lucrative market for unique digital asset ownership verification, including digital artworks. However, this trend has also given rise to concerns such as fraud, stolen works, authenticity, and copyright issues. Illicit traders exploit the market by trading unauthorized copies of digital objects as NFTs. In this study, we propose the use of digital watermarking as a means to establish the authenticity of NFTs and enhance the marketplace’s credibility.
Open access
Advanced Steganography and Watermarking Techniques
Recently, the intellectual property protection methods based on deep learning have achieved great success, but there are still serious infringement issues that the network topology or hyper parameters of the trained model are stolen by third parties. In this paper, we construct a deep learning model based on the autoencoder to remove the bone from the medical images containing chest x-ray, and the specific trigger set is trained and predicted to get the effect of the backdoor watermark. The scheme of zero-knowledge proof is applied to transform the backdoor watermark of the model into the fixed-length string, which is published in the block chain to verify the ownership of the model. Through the non-interactive verification between the model owner and the third party, the ownership of the model can be confirmed by the third party and the verification process will not disclose any information of the model itself. The method proposed in this paper can support infinite times of verification and does not reveal any information about the model, so as to achieve the protection of intellectual property rights of the model.
Adversarial Robustness in Machine Learning
Generative Adversarial Networks and Image Synthesis