Blockchain Papers

Qingliang Liu, Shuguo Yang, Jing Liu, Li Zhao · 6 authors

No abstract is available for this record.

Sarah Khadijah Taylor, Steve Ho-yong Kim, Khairul Akram Zainol Ariffin, Siti Norul Huda Sheikh Abdullah

Studies have shown that the existing methodology of digital forensics preservation, which is to acquire and hash the evidence, is insufficient for cryptocurrencies as it does not secure the value. To address this issue, investigators secure the cryptocurrency by transferring it to a crypto wallet controlled by the Law Enforcement Agencies(LEAs). This process will unavoidably modify some data. Despite the criticality of this issue, inadequate studies have been made in this area. In addition, current guidelines on securing the cryptocurrency lack a comprehensive description from the perspective of digital evidence preservation principles. Crucial data to be documented throughout the preservation process were also not properly listed. Therefore, this study aims to address the gap in preserving cryptocurrencies from crypto wallets. Three objectives were then laid out; (1) to develop a methodology that is mapped comprehensively with digital evidence preservation principle, (2) to describe and provide justification on the inevitably modified data, and (3) to list crucial data to be documented during preservation process. The methods to achieve the objectives were critical examinations on various types of crypto wallets and by using simulation. The result shows that the study is able to provide a comprehensive crypto wallets preservation methodology to forensic investigators. It is hoped that the outcome from this study will promote better understanding, ensure consistency of implementation, and to aid investigators in explaining and justifying their actions during search and seizure in court.

Rohit Gandikota, Nik Bear Brown

Art is an artistic method of using digital technologies as a part of the generative or creative process. With the advent of digital currency and NFTs (Non-Fungible Token), the demand for digital art is growing aggressively. In this manuscript, we advocate the concept of using deep generative networks with adversarial training for a stable and variant art generation. The work mainly focuses on using the Deep Convolutional Generative Adversarial Network (DC-GAN) and explores the techniques to address the common pitfalls in GAN training. We compare various architectures and designs of DC-GANs to arrive at a recommendable design choice for a stable and realistic generation. The main focus of the work is to generate realistic images that do not exist in reality but are synthesised from random noise by the proposed model. We provide visual results of generated animal face images (some pieces of evidence showing a blend of species) along with recommendations for training, architecture and design choices. We also show how training image preprocessing plays a massive role in GAN training.

Mohamed Allouche, Mihai Mitrea

The last decades have seen video production and consumption rise significantly: TV/cinematography, social networking, digital marketing, and video surveillance incrementally and cumulatively turned video content into the predilection type of data to be exchanged, stored, and processed. Belonging to video processing realm, video fingerprinting (also referred to as content-based copy detection or near duplicate detection ) regroups research efforts devoted to identifying duplicated and/or replicated versions of a given video sequence (query) in a reference video dataset. The present paper reports on a state-of-the-art study on the past and present of video fingerprinting, while attempting to identify trends for its development. First, the conceptual basis and evaluation frameworks are set. This way, the methodological approaches (situated at the cross-roads of image processing, machine learning, and neural networks) can be structured and discussed. Finally, fingerprinting is confronted to the challenges raised by the emerging video applications ( e.g. , unmanned vehicles or fake news) and to the constraints they set in terms of content traceability and computational complexity. The relationship with other technologies for content tracking ( e.g., DLT - Distributed Ledger Technologies) are also presented and discussed.

Yi Chen, Chung-Chiang Chen, Li‐Chuan Tang, Wei-Hua Chieng

Abstract NuText is a novel music-encoding technology based on numbered musical notation. This paper outlines the notation principles of numbered musical notation and delineates the conversion relationship and encoding protocol between NuText and numbered musical notation. Furthermore, this study demonstrates NuText's playback software and its practical applications, including digital artwork creation, Non-Fungible Tokens (NFTs), and social use, and identifies opportunities to develop it as a music technology. The encoding method proposed herein was implemented on PCs and mobile devices, and the method has been successfully applied to sports-oriented music. The image steganography method used in digital artwork creation, Non-Fungible Tokens (NFTs), and social use does not destroy images, and it has been implemented on mobile devices. NuText is a note-level encoding method, which has advantages for interpreting music connotations and a great potential in the development of music information retrieval and artificial intelligence composition. In future work, special musical skills may be added, including the modification of each note velocity, and this may be incorporated into the encoding specification to realize sound in virtual reality.

Zichi Wang, Xin Feng, Xinpeng ZHANG

The images with non-fungible token (NFT) are employed as the digital artistic works in metaverse for creation, transaction, sharing, and collection.Being different from natural images, the content of NFT images is defined by user and distributed in the digital space widely.It is convenient for the hidden of secret data.In this case, covert communication with NFT images is a new branch of image steganography.Then, a steganographic method for NFT images was proposed accordingly.Given a NFT image, the regions of its profile and the components with high frequency were enhanced firstly to enrich the details which were beneficial to hide the modification trace of steganography.In this way, the enhanced image was used as cover since it is more suitable for steganography.Then, the tendency modification direction of each pixel was determined by the differences between the enhanced image and the given image.The differences were also used to determine the cost value of modification amplitude.Thus, the undetectability of steganography can be increased further.Secret data was embedded into the cover image using the popular steganographic coding schemes.Experimental results showed that the proposed method had imporoved undetectability on NFT images compared with existing digital steganographic schemes.Compared with HILL, MiPOD, and DEFI, the proposed method can increase the detection error P_E of steganalysis by 8.7%, 9.2% and 6.2%, respectively (the average value for the cases of different payload and steganalytic features).Therefore, the proposed method is suitable for NFT images and it provides targeted steganographic method for the third kind of images, i.e., NFT images, except of natural images and generated images.For further study, the deep learning-based steganographic method can be designed for NFT images using the strong fitting and learning ability of neural networks.

Frederik Temmermans, Deepayan Bhowmik, Fernando Pereira, Touradj Ebrahimi

Advances in deep neural networks (DNN) and distributed ledger technology (DLT) have shown major influence on media security, authenticity and privacy. Current deepfake techniques can produce near realistic media content which can be used in both good and bad intended use cases. At the same time, DLTs are finding their way in the industry as fair, transparent and reliable means for content distribution. In particular non-fungible tokens (NFTs) are emerging in the digital art market. However, such new developments also introduce new challenges, including the need for robust and reliable metadata, a mechanism to secure the media and associated metadata, means to verify authenticity and interoperability between various stakeholders. This paper identifies emerging challenges in fake media and NFT, and proposes a novel framework to effectively cope with secure media applications allowing for a structured, systematic, and interoperable solution. The framework relies on an architecture that is modular, flexible, extensible, and scalable in the sense that it can be implemented in both lighter as well as more feature-rich and more complex configurations depending on the underlying application, needed features and available resources, while enabling products and services in various ecosystems with desired trust and security capabilities. The framework is inspired by activities and developments within JPEG standardisation related to security, authenticity and privacy.

Francesco Zola, Lander Segurola-Gil, Jan L. Bruse, Mikel Galar · 5 authors

Abstract Classification of Bitcoin entities is an important task to help Law Enforcement Agencies reduce anonymity in the Bitcoin blockchain network and to detect classes more tied to illegal activities. However, this task is strongly conditioned by a severe class imbalance in Bitcoin datasets. Existing approaches for addressing the class imbalance problem can be improved considering generative adversarial networks (GANs) that can boost data diversity. However, GANs are mainly applied in computer vision and natural language processing tasks, but not in Bitcoin entity behaviour classification where they may be useful for learning and generating synthetic behaviours. Therefore, in this work, we present a novel approach to address the class imbalance in Bitcoin entity classification by applying GANs. In particular, three GAN architectures were implemented and compared in order to find the most suitable architecture for generating Bitcoin entity behaviours. More specifically, GANs were used to address the Bitcoin imbalance problem by generating synthetic data of the less represented classes before training the final entity classifier. The results were used to evaluate the capabilities of the different GAN architectures in terms of training time, performance, repeatability, and computational costs. Finally, the results achieved by the proposed GAN-based resampling were compared with those obtained using five well-known data-level preprocessing techniques. Models trained with data resampled with our GAN-based approach achieved the highest accuracy improvements and were among the best in terms of precision, recall and f1-score. Together with Random Oversampling (ROS), GANs proved to be strong contenders in addressing Bitcoin class imbalance and consequently in reducing Bitcoin entity anonymity (overall and per-class classification performance). To the best of our knowledge, this is the first work to explore the advantages and limitations of GANs in generating specific Bitcoin data and “attacking” Bitcoin anonymity. The proposed methods ultimately demonstrate that in Bitcoin applications, GANs are indeed able to learn the data distribution and generate new samples starting from a very limited class representation, which leads to better detection of classes related to illegal activities.

Iynkaran Natgunanathan, Purathani Praitheeshan, Longxiang Gao, Yong Xiang · 5 authors

Copyright protection in multimedia protection distribution is a challenging problem. To protect multimedia data, many watermarking methods have been proposed in the literature. However, most of them cannot be used effectively in a multimedia distribution network (MDN) as they are not designed to support multi-layer watermark embedding. Multi-layer watermarking mechanisms were developed to protect multimedia data across different layers in an MDN. However, in those mechanisms, we need to trust the entities in the MDN, such as regional and country distributors. To overcome this potential drawback, in this article, we propose a novel privacy protection mechanism for MDNs by combining the advantages of both blockchain and watermarking technologies. A specifically designed watermarking algorithm is used to link the copyright information with the audio file, while a novel blockchain-based smart contract mechanism is developed to enforce the proper functioning of each entity in the distribution network. Moreover, the new audio mechanism is computationally efficient. Although audio signals are used to show the effectiveness of the proposed mechanism, the proposed approach can easily be extended to other multimedia objects, such as an image. The validity of the proposed mechanism is demonstrated by our simulation results. The proposed mechanism can benefit multimedia production companies and other entities in the MDN.

Mohamed Al Ali, Ahmed Adel Ismail, Hany M. Elgohary, Saad M. Darwish · 5 authors

Digital evidence is critical in cybercrime investigations because it is used to connect individuals to illegal activity. Digital evidence is complicated, diffuse, volatile, and easily altered, and as such, it must be protected. The Chain of Custody (CoC) is a critical component of the digital evidence procedure. The aim of the CoC is to demonstrate that the evidence has not been tampered with at any point throughout the investigation. Because the uncertainty associated with digital evidence is not being assessed at the moment, it is impossible to determine the trustworthiness of CoC. As scientists, forensic examiners have a responsibility to reverse this tendency and officially confront the uncertainty inherent in any evidence upon which they base their judgments. To address these issues, this article proposes a new paradigm for ensuring the integrity of digital evidence (CoC documents). The new paradigm employs fuzzy hash within blockchain data structure to handle uncertainty introduced by error-prone tools when dealing with CoC documents. Traditional hashing techniques are designed to be sensitive to small input modifications and can only determine if the inputs are exactly the same or not. By comparing the similarity of two images, fuzzy hash functions can determine how different they are. With the symmetry idea at its core, the suggested framework effectively deals with random parameter probabilities, as shown in the development of the fuzzy hash segmentation function. We provide a case study for image forensics to illustrate the usefulness of this framework in introducing forensic preparedness to computer systems and enabling a more effective digital investigation procedure.

Pietro Manganelli Conforti, matteo emanuele, Pietro Nardelli, Giuseppe Santucci · 5 authors

No abstract is available for this record.

Sakib Shahriar, Kadhim Hayawi

Digital arts have gained an unprecedented level of popularity with the emergence of non-fungible tokens (NFTs). NFTs are cryptographic assets that are stored on blockchain networks and represent a digital certificate of ownership that cannot be forged. NFTs can be incorporated into a smart contract which allows the owner to benefit from a future sale percentage. While digital art producers can benefit immensely with NFTs, their production is time consuming. Therefore, this paper explores the possibility of using generative adversarial networks (GANs) for automatic generation of digital arts. GANs are deep learning architectures that are widely and effectively used for synthesis of audio, images, and video contents. However, their application to NFT arts have been limited. In this paper, a GAN-based architecture is implemented and evaluated for novel NFT-style digital arts generation. Results from the qualitative case study indicate that the generated artworks are comparable to the real samples in terms of being interesting and inspiring and they were judged to be more innovative than real samples.

SwapnaliTambe, Anil Pawar, Santosh Kumar Yadav

Deepfake is as a matter of fact a medium where one individual is supplanted by another who appears as though him. The profound bogus demonstration has been continuing for quite a long while. Profound phony uses incredible strategies, for example, AI and man-made consciousness to create and control visual and sound substance with high potential for the gadget. Profound misrepresentation relies upon the sort of impartial association called and the programmed encoder. These are essential for an encoder, which lessens a picture to a lower dimensional ideal and an ideal introduction picture. I examined various answers on various advances via web-based media stages like twitter and face book. From these examinations we are roused to extend this objective. In our proposed framework, we centre around identifying profound phony recordings utilizing blockchains, keen agreements, and secure hashing calculations. We utilize a few calculations to relieve the issue, for example, the SHA string

Jernej Vičič, Aleksandar Tošić

The manuscript presents a study of the possibility of use of Benford’s law conformity test, a well proven tool in the accounting fraud discovery, on a new domain: the discovery of anomalies (possibly fraudulent behaviour) in the the cryptocurrency transactions. Blockchain-based currencies or cryptocurrencies have become a global phenomenon known to most people as a disruptive technology, and a new investment vehicle. However, due to their decentralized nature, regulating these markets has presented regulators with difficulties in finding a balance between nurturing innovation, and protecting consumers. The growing concerns about illicit activity have forced regulators to seek new ways of detecting, analyzing, and ultimately policing public blockchain transactions. Extensive research on machine learning, and transaction graph analysis algorithms has been done to track suspicious behaviour. However, having a macro view of a public ledger is equally important before pursuing a more fine-grained analysis. Benford’s law, the law of first digit, has been extensively used as a tool to discover accountant frauds (many other use cases exist). The basic motivation that drove our research presented in this paper was to test the applicability of the well established method to a new domain, in this case the identification of anomalous behavior using Benford’s law conformity test to the cryptocurrency domain. The research focused on transaction values in all major cryptocurrencies. A suitable time-period was identified that was long enough to present sufficiently large number of observations for Benford’s law conformity tests and was also situated long enough in the past so that the anomalies were identified and well documented. The results show that most of the cryptocurrencies that did not conform to Benford’s law had well documented anomalous incidents, the first digits of aggregated transaction values of all well known cryptocurrency projects were conforming to Benford’s law. Thus the proposed method is applicable to the new domain.

Mustafa Takaoğlu, Adem Özyavaş, Naim Ajlouni, Ali Alshahrani · 5 authors

Data security and data hiding have been studied throughout history. Studies show that steganography and encryption methods are used together to hide data and avoid detection. Large amounts of data hidden in the cover multimedia distort the image, which can be detected in visual and histogram analysis. The proposed method will solve two major drawbacks of the current methods: the limitation imposed on the size of the data to be hidden in the cover multimedia and low resistance to steganalysis after stego-operation. In the proposed method, plaintext data are divided into fixed-sized bits whose corresponding matching bits’ indices in the cover multimedia are accumulated. Thus, the hidden data are composed of the indices in the cover multimedia, causing no change in it, thus enabling considerable amounts of plaintext to be hidden. The proposed method also has high resistance to known steganalysis methods because it does not cause any distortion to the cover multimedia. The test results show that the performance of the proposed method outperforms similar conventional stenographic techniques. The proposed Ozyavas–Takaoglu–Ajlouni (OTA) method relieves the limitation on the size of the hidden data, and hidden data is undetectable by steganalysis because it is no longer embedded in the cover multimedia.

Srivatsav Chenna, Nils Peters

Detection of fabricated or manipulated audio content to prevent, e.g., distribution of forgeries in digital media, is crucial, especially in political and reputational contexts. Better tools for protecting the integrity of media creation are desired. Within the paradigm of the Internet of Audio Things(IoAuT), we discuss the ability of the IoAuT network to verify the authenticity of original audio using distributed ledger technology. By storing audio recordings in combination with associated recording-specific metadata obtained by the IoAuT capturing device, this architecture enables secure distribution of original audio footage, authentication of unknown audio content, and referencing of original audio material in future derivative works. By developing a proof-of-concept system, the feasibility of the proposed architecture is evaluated and discussed.

Ji-Hwei Horng, Ching‐Chun Chang, Guanlong Li, Wai‐Kong Lee · 5 authors

Medical images carry a lot of important information for making a medical diagnosis. Since the medical images need to be communicated frequently to allow timely and accurate diagnosis, it has become a target for malicious attacks. Hence, medical images are protected through encryption algorithms. Recently, reversible data hiding on the encrypted images (RDHEI) schemes are employed to embed private information into the medical images. This allows effective and secure communication, wherein the privately embedded information (e.g., medical records and personal information) is very useful to the medical diagnosis. However, existing RDHEI schemes still suffer from low embedding capacity, which limits their applicability. Besides, such solution still lacks a good mechanism to ensure its integrity and traceability. To resolve these issues, a novel approach based on image block-wise encryption and histogram shifting is proposed to provide more embedding capacity in the encrypted images. The embedding rate is over 0.8 bpp for typical medical images. On top of that, a blockchain-based system for RDHEI is proposed to resolve the traceability. The private information is stored on the blockchain together with the hash value of the original medical image. This allows traceability of all the medical images communicated over the proposed blockchain network.

Aaron Zimba, Christabel Ngongola-Reinke, Mumbi Chishimba, Tozgani Fainess Mbale

Cryptocurrencies have emerged as a new form of digital money that has not escaped the eyes of cyber-attackers. Traditionally, they have been maliciously used as a medium of exchange for proceeds of crime in the cyber dark-market by cyber-criminals. However, cyber-criminals have devised an exploitative technique of directly acquiring cryptocurrencies from benign users' CPUs without their knowledge through a process called crypto mining. The presence of crypto mining activities in a network is often an indicator of compromise of illegal usage of network resources for crypto mining purposes. Crypto mining has had a financial toll on victims such as corporate networks and individual home users. This paper addresses the detection of crypto mining attacks in a generic network environment using dynamic network characteristics. It tackles an in-depth overview of crypto mining operational details and proposes a semi-supervised machine learning approach to detection using various crypto mining features derived from complex network characteristics. The results demonstrate that the integration of semi-supervised learning with complex network theory modeling is effective at detecting crypto mining activities in a network environment. Such an approach is helpful during security mitigation by network security administrators and law enforcement agencies.

Sina Rafati Niya, Benjamin Jeffrey, Burkhard Stiller

The integration of Internet-of-Things (IoT) and Blockchains (BC) for trusted and decentralized approaches enabled modern use cases, such as supply chain tracing, smart cities, and IoT data marketplaces. For these it is essential to identify reliably IoT devices, since the producer-consumer trust is not guaranteed by a Trusted Third Party (TTP). Therefore, this work proposes a Know Your IoT device platform (KYoT), which enables the self-sovereign identification of IoT devices on the Ethereum BC. KYoT permits manufacturers and device owners to register and verify IoT devices in a self-sovereign fashion, while data storage security is ensured. KYoT deploys an SRAM-based (Static Random Access Memory) Physically Unclonable Function (PUF), which takes advantage of the manufacturing variability of devices' SRAM chips to derive a unique identifying key for each IoT device. The self-sovereign identification mechanism introduced is based on the ERC 734 and ERC 735 Ethereum identity standards.

Sagar Rao, Shalomi Fernandes, Samruddhi Raorane, Shafaque Fatma Syed

No abstract is available for this record.

Juan Zhao, Tianrui Zong, Yong Xiang, Longxiang Gao · 5 authors

No abstract is available for this record.

Ui-Jun Baek, Se-Hyun Ji, Jee- Tae Park, Min‐Seob Lee · 6 authors

Since the inception of Bitcoin, the first cryptocurrency to implement blockchain technology, the cryptocurrency market has experienced significant growth.However, this growth has also brought about numerous vulnerabilities and attacks that pose a threat to the Bitcoin ecosystem.These attacks are not only focused on the Bitcoin network itself but also extend to the services that utilize it.Recent surveys have indicated the need to analyze and identify Distributed Denial of Service (DDoS) attacks, considering the interconnectedness between network-level data and service-level DDoS attacks within the Bitcoin system.Typically, the Bitcoin network is considered resilient against DDoS attacks due to the decentralized nature of its ledger.Nevertheless, there are potential vulnerabilities that could be exploited, such as message spoofing using the Transmission Control Protocol (TCP).Additionally, DDoS attacks often target services associated with Bitcoin usage rather than directly impacting the network's performance or stealing currency.Although these service-level attacks may not have an immediate impact, they can ultimately undermine the value of Bitcoin, leading to depreciation.The majority of DDoS attacks on Bitcoin-related services occur on exchanges and mining pools.Our approach involves evaluating experimental outcomes based on proposed metrics to establish a correlation between network-level data and service-level DDoS attacks in the Bitcoin system.By doing so, we aim to detect and analyze these attacks, thereby identifying potential associations.Furthermore, we posit that the methodology employed in this study could be applicable to other blockchain systems, extending its usefulness beyond the Bitcoin network.

Ankit Gangwal, Samuele Giuliano Piazzetta, Gianluca Lain, Mauro Conti

Cybercriminals have been exploiting cryptocurrencies to commit various unique financial frauds. Covert cryptomining - which is defined as an unauthorized harnessing of victims' computational resources to mine cryptocurrencies - is one of the prevalent ways nowadays used by cybercriminals to earn financial benefits. Such exploitation of resources causes financial losses to the victims. In this paper, we present our novel and efficient approach to detect covert cryptomining. Our solution is a generic solution that, unlike currently available solutions to detect covert cryptomining, is not tailored to a specific cryptocurrency or a particular form of cryptomining. In particular, we focus on the core mining algorithms and utilize Hardware Performance Counters (HPC) to create clean signatures that grasp the execution pattern of these algorithms on a processor. We built a complete implementation of our solution employing advanced machine learning techniques. We evaluated our methodology on two different processors through an exhaustive set of experiments. In our experiments, we considered all the cryptocurrencies mined by the top-10 mining pools, which collectively represent the largest share (84% during Q3 2018) of the cryptomining market. Our results show that our classifier can achieve a near-perfect classification with samples of length as low as five seconds. Due to its robust and practical design, our solution can even adapt to zero-day cryptocurrencies. Finally, we believe our solution is scalable and can be deployed to tackle the uprising problem of covert cryptomining.

William Thomas Weilbach, Yusuf Moosa Motara

This paper examines the way in which blockchain technology can be used to improve the verification of integrity of evidence in digital forensics. Some background into digital forensic practices and blockchain technology are discussed to provide necessary context. A particular scalable method of verifying point-in-time existence of a piece of digital evidence, using the OpenTimestamps (OTS) service, is described, and tests are carried out to independently validate the claims made by the service. The results demonstrate that the OTS service is highly reliable with a zero false positive and false negative error rate for timestamp attestations, but that it is not suitable for timesensitive timestamping due to the variance of the accuracy of timestamps induced by block confirmation times in the Bitcoin blockchain.