Blockchain Papers

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.

Pei Zhang, Qingfeng Cheng, Mingliang Zhang, Xiangyang Luo

With the rapid development of the Internet, it has become a hot spot in protecting important information from illegal theft and destruction during network transmission. The characteristics of strong robustness, anonymity and distributed transmission in blockchain technology can precisely make up for the disadvantages faced by traditional covert communication in transmitting important data, such as poor robustness, easy exposure of participants and difficulty in achieving group covert transmission. Therefore, a group steganography method for digital currency based on blockchain technology is proposed in this paper. In this method, an index matrix of address interaction is designed for group covert communication, using the address interaction relationship and transaction amount to hide secret message alternately, the transaction hiding secret message is published on the blockchain. In different groups, the receivers use different keys to extract different secret message. The method proposed also introduces a fine-grained group division mechanism to control the spread of secret information and reduce the risk of secret information leakage. And the application scenarios of the method proposed are more universal, breaking the limitation of using a single digital currency for covert communication on the blockchain. In addition, the proposed method modulates the number of addresses reasonably by sensing the generation time of the block, enhancing the concealment of the method. Through practical tests on a variety of mainstream digital currencies, the experimental results show that digital currencies with smaller block generation times need to consume less embedding time.

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.

Ankala Stallin, Kagga Pavan Kumar, B. Prabha

Digital media is one of the most shared, influenced and highly used forms of the medium in the 21stcentury. Its presence ranges from simple images, shared in a Peer-to-Peer network to a world-renowned newspaper that provides second to second update on events around the world at the comfort zone. With this ease-of-access and anonymity it is easier than ever to copy and claim someone else’s work as their own. This nefarious practice of copyright infringement has high stakes when monetization is involved, especially in a high-stake venture such as NFT (Non-fungible Token) based artworks that are not only meant to be unique but also should benefit the artists not the people who masquerades as the true owner of the said art. To avoid such malpractices, watermarking is one of the oldest yet most used methods of authentication techniques for images. It is more effective if it’s hidden in plain sight (most of the times within the image’s meta-data,) so that it wouldn’t affect the appearance of the image but provides the impact, nonetheless. Discrete Cosine Transform (DCT) algorithm based hidden image watermarking is implemented to focus more on domain frequency. Furthermore, Domain Frequency is extracted to transform the original image and to add the watermark. DCT is implemented to enhance the rate of watermark processing. The whole project, in their individual modules discuss Image processing, encoding the said output with the watermark image and decrypting the said resultant to verify the watermark. As said before, the applications are vast with the emerging technology of Web3 and blockchain backed projects.

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.

Keshav Kaushik, Susheela Dahiya, Rewa Sharma

Blockchain is a progression of associated information structures called blocks, which contain or track all that occurs in disseminated frameworks in a distributed organization. Each block is connected to the previous block with an uncommon pointer called a hash pointer, forming a chain and resulting in a framework consisting of annexes: A perpetual and irreversible history that can be utilized as a constant review trail by any member to check the precision of the records by essentially surveying information itself. The chapter will discuss the role of blockchain in digital forensics with an introduction to blockchain technology and its applications and challenges. It also explores the architecture and protocols related to blockchain technology. The chapter also highlights the managing of digital evidence by maintaining the chain of custody with the help of Ethereum and Hyperledger. It will also enlighten readers about the application of blockchain for distributed cloud storage in digital forensics. It will reveal the role of blockchain in digital forensics, which will be helpful for cybercrime investigation, blockchain technology, and digital forensics enthusiasts, students, PhD scholars and researchers.

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.

Richard Tindell, Alex J. Mitchell, Nathan Sprague, Xunhua Wang

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.

Deeraj Nagothu, Ronghua Xu, Yu Chen, Erik Blasch · 5 authors

Rapid advances in the Internet of Video Things (IoVT) deployment in modern smart cities has enabled secure infrastructures with minimal human intervention. However, attacks on audio-video inputs affect the reliability of large-scale multimedia surveillance systems as attackers are able to manipulate the perception of live events. For example, Deepfake audio/video attacks and frame duplication attacks can cause significant security breaches. This paper proposes a Lightweight Environmental Fingerprint Consensus based detection of compromised smart cameras in edge surveillance systems (LEFC). LEFC is a partial decentralized authentication mechanism that leverages Electrical Network Frequency (ENF) as an environmental fingerprint and distributed ledger technology (DLT). An ENF signal carries randomly fluctuating spatio-temporal signatures, which enable digital media authentication. With the proposed DLT consensus mechanism named Proof-of-ENF (PoENF) as a backbone, LEFC can estimate and authenticate the media recording and detect byzantine nodes controlled by the perpetrator. The experimental evaluation shows feasibility and effectiveness of proposed LEFC scheme under a distributed byzantine network environment.

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.

Paul Black, Iqbal Gondal, Richard R. Brooks, Lu Yu

News media often contain reports that raise doubt related to policing operations. We examine the question of how to improve policing integrity during the execution of search warrants and provide an outline for law enforcement search warrants and digital forensic analysis procedures. Existing techniques for improving the integrity of search warrants are reviewed, limitations are noted, and we propose an Advanced Forensic Evidence System (AFES) to address these limitations.AFES provides an immutable record and biometric authentication of the officers present during the execution of a search warrant, time and location, video recording, seizure record, contemporaneous notes, and photographs. AFES records digital evidence items, imaging details, evidence hashes, provides an access control system, and an immutable record of access to all stored items. AFES uses a permissioned distributed ledger prototype, called Scrybe, developed under NSF aegis, to ensure evidence seizure integrity. Scrybe is run as multiple blockchain instances at law enforcement, prosecution, judicial, and defence organisations to ensure that an immutable record is maintained.

Ujwal Patil, P. M. Chouragade

Nowadays, it is difficult to predict the information such as news and videos on the internet is real or not and people are increasingly sharing it on social media without thinking of fake. As soon as they verify the authenticity of a video, they start expressing their concerns and sharing the opinions of others. Therefore, such videos spread rapidly and help in sharing fake videos or unverified information. The need today is to focus on building a strong fake video detection system as soon as possible to avoid the consequences of such unverified information. This paper analyzes the recent research articles to detect the deep fake videos on social media.

Xiangyang Luo, Pei Zhang, Mingliang Zhang, Hao Li · 5 authors

With the global promotion and application of 5G technology, the data transmitted on the network show explosive growth, and the secure sharing of its important data is still one of the research hotspots. Steganography embeds the data that needs to be shared into digital carrier files and transmits it through open channels, which has important applications in protecting data sharing and realizing covert communication. However, the encrypted files generated by traditional steganography are susceptible to compression, cropping, geometric attacks, and man-made destruction in the process of open channel transmission, resulting in data loss, making it difficult for the receiver to correctly extract secret message. Currently, covert communication on blockchain can solve the above problems, but it also brings some new problems such as high computational complexity, low transmission efficiency, and nondetection resistance. Therefore, in this article, a covert communication method based on Bitcoin transactions is proposed. The proposed method first designs the index matrix of the transaction address. Then, the address interaction relationship that carries the secret message through the transaction index matrix is constructed. Finally, the address interaction relationship that carries the secret message is combined with the transaction amount to complete the covert transmission of the secret message on the blockchain environment. The proposed method improves the security and embedding efficiency of covert communication, reduces the number of transactions, and ensures the integrity of extracting secret message. A series of experimental results show that under the condition of ensuring the necessary security, the proposed method retains the strong robustness of the existing blockchain steganography and has strong resistance to detection.

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.

Randhir Kumar, Rakesh Tripathi, Ningrinla Marchang, Gautam Srivastava · 6 authors

No abstract is available for this record.

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.

Mohd Zaki Mas’ud, Aslinda Hassan, Wahidah Md Shah, Shekh Faisal Abdul‐Latip · 7 authors

A blockchain can be summarized as a decentralized ledger of all transactions across a peer-to-peer network. It is the primary technology behind the large number of diverse cryptocurrencies that are currently available in circulation. Since its introduction, blockchain technology has shown promising application prospects and attracted much attention from academia and industry. It also has become an obvious target for adversaries. In this paper, we conduct a review of the implementation of digital forensic investigation processes to blockchain and cryptocurrency. Based on our findings, we can conclude that digital forensics is still considered a new area for blockchain technology, especially in cryptocurrency.