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Nicola Noviello, Remo Pareschi
No abstract is available for this record.
Rasha Almajed, Abedallah Zaid Abualkishik, Amer M. Ibrahim, Nahia Mourad
Non-Fungible Tokens (NFTs) are one-of-a-kind digital items with static or continuous visual and audio content. NFTs digitally represent any assets that may hold photos, gifs, audio, videos, or any other data-based storable material. These assets may come under a variety of asset groups, including art, in-game goods, and entertainment collecting units. What makes them appealing is their exclusivity, in the sense that each NFT is unique to itself, and ownership is determined by a digital certificate. In the first half of 2021, NFT sales totaled more than a billion. The NFT Software as a service (SAAS) based system is a one-of-a-kind offering and concept for thinking outside the box and presenting intellectuals and creative treasures and exhibiting these objects to ensure the security and integrity of digital assets. The existence of core decentralized networks allows for unrestricted access to this material as well as further analysis. Based on the Web3 Blockchain technology, these assets may be traded and represent next-generation ownership. In this paper, Adaptive Improved Convolutional Neural Networks (AICNN) are used to forecast NFT to provide a SAAS NFT collector. We also introduce Tree-seed Chaotic Atom Search Optimization (TSC-ASO) algorithm to optimize the forecasting process. The proposed method of NFT price forecasting is evaluated and compared with the existing forecasting methods. To produce an accurate report for NFT price forecasting, the proposed method will be effective.
Sina Osivand
Metaverse is an immersive 3D virtual environment, a true virtual artificial community in which avatars act as the user's alter ego and interact with each other. If we do not manage the hype for the metaverse, which has recently been receiving a surge in interest, the metaverse will fail to cross the chasm. This article conducts a comprehensive survey on computational arts, in which seven critical topics are relevant to the metaverse, describing novel artworks in blended virtual-physical realities. The topics first cover the building elements for the metaverse, e.g. Virtual scenes and characters, auditory, textual elements. Next, several remarkable types of novel creations in the expanded horizons of metaverse cyberspace have been reflected, such as immersive arts, robotic arts, and other user-centric approaches fuelling contemporary creative outputs.
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
Samuel Rikli, Nico, Bigler Daniel, Moritz Pfenninger, Joerg, Osterrieder
Modeling financial time series is challenging due to their high volatility and unexpected happenings on the market. Most financial models and algorithms trying to fill the lack of historical financial time series struggle to perform and are highly vulnerable to overfitting. As an alternative, we introduce in this paper a deep neural network called the WGAN-GP, a data-driven model that focuses on sample generation. The WGAN-GP consists of a generator and discriminator function which utilize an LSTM architecture. The WGAN-GP is supposed to learn the underlying structure of the input data, which in our case, is the Bitcoin. Bitcoin is unique in its behavior; the prices fluctuate what makes guessing the price trend hardly impossible. Through adversarial training, the WGAN-GP should learn the underlying structure of the bitcoin and generate very similar samples of the bitcoin distribution. The generated synthetic time series are visually indistinguishable from the real data. But the numerical results show that the generated data were close to the real data distribution but distinguishable. The model mainly shows a stable learning behavior. However, the model has space for optimization, which could be achieved by adjusting the hyperparameters.
Jun Feng, Laurence T. Yang, Yuxiang Zhu, Nicholaus J. Gati · 5 authors
Deep learning techniques have shown significant success in cyber-physical-social systems (CPSS). As an instance of deep learning models, generative adversarial nets (GAN) model enables powerful and flexible image augmentation, image generation, and classification, thus can be applied to real-world CPSS settings. GAN model training needs a large collection of cyber-physical-social data originating from various CPSS devices. Numerous prevailing GAN models depend on a tacit assumption that several cyber-physical-social data providers present a reliable source to collect training data, which is seldom the case in real CPSS. The existing GAN models also fail to consider multi-dimensional latent structure. In our work, we put forward a novel blockchain-enabled tensor-based conditional deep convolutional GAN (TCDC-GAN) model for cyber-physical-social systems. The blockchain is employed to develop a decentralized and reliable cyber-physical-social data-sharing platform between numerous cyber-physical-social data providers, such that the training data and the model are documented on a ledger that is distributed. Furthermore, a tensor-based generator and a tensor-based discriminator are well designed by employing the tensor model. The results of extensive simulation experiments show the efficacy of the proposed TCDC-GAN model. Compared with the state-of-the-art models, our model gains superior estimation performance.
Randhir Kumar, Rakesh Tripathi, Ningrinla Marchang, Gautam Srivastava · 6 authors
No abstract is available for this record.
Christopher Chun Ki Chan, Vimal Kumar, Steven Delaney, Munkhjargal Gochoo
Malicious use of deep learning algorithms has allowed the proliferation of high realism fake digital content such as text, images, and videos, to exist on the internet as readily available and accessible consumable content. False information provided through algorithmically modified footage, images, audios, and videos (known as deepfakes), coupled with the virality of social networks, may cause major social unrest. The emergence of misinformation from fabricated digital content suggests the necessity for anti-disinformation methods such as deepfake detection algorithms or immutable metadata in order to verify the validity of digital content. Permissioned blockchain, notably Hyperledger Fabric 2.0, coupled with LSTMs for audio/video/descriptive captioning is a step towards providing a feasible tool for combating deepfake media. Original content would require the original artist attestation of untampered data. The smart contract combines a varied multiple LSTM networks into a process that allows for the tracing and tracking of a digital content's historical provenance. The result is a theoretical framework that enables proof of authenticity (PoA) for digital media using a decentralized blockchain using multiple LSTMs as a deep encoder for creating unique discriminative features; which is then compressed and hashed into a transaction. Our work assumes we trust the video at the point of reception. Our contribution is a decentralized blockchain framework of deep discriminative digital media to combat deepfakes.
Sébastien Ragot, Antje Rey, Ramin Shafai
No abstract is available for this record.
Tu Bui, Daniel Cooper, John Collomosse, Mark Bell · 10 authors
We present ARCHANGEL; a novel distributed ledger based system for assuring the long-term integrity of digital video archives. First, we introduce a novel deep network architecture using a hierarchical attention autoencoder (HAAE) to compute temporal content hashes (TCHs) from minutes or hour-long audio-visual streams. Our TCHs are sensitive to accidental or malicious content modification (tampering). The focus of our self-supervised HAAE is to guard against content modification such as frame truncation or corruption but ensure invariance against format shift (i.e. codec change). This is necessary due to the curatorial requirement for archives to format shift video over time to ensure future accessibility. Second, we describe how the TCHs (and the models used to derive them) are secured via a proof-of-authority blockchain distributed across multiple independent archives. We report on the efficacy of ARCHANGEL within the context of a trial deployment in which the national government archives of the United Kingdom, United States of America, Estonia, Australia and Norway participated.
Zhini Cai
In order to explore the application of deep learning algorithms in arrangement and composition, and the role of blockchain in the protection of digital music copyright, a monophonic melody composition model based on the deep generative adversarial networks (DCGANs) is constructed firstly, and the composition performance of the model is analyzed using hymn as input sample in this study. Later, the multi-instrument co-arrangement (MICA) model based on the multi-task learning is proposed, and the composition performance is analyzed by taking the actual music as an input sample. Finally, the improved practical byzantine fault tolerance (IPBFT) algorithm is proposed, and a digital music copyright protection system is designed based on the blockchain in this study. The results indicate that the accuracies constructed DCGANs model in predicting the Soprano and Alto voice melody are higher than those of the DeepBatch model by 2.29% and 3.32%, respectively. The performance on the harmony score, note accuracy, Levenshtein similarity (LS), notes distribution mean square error, and empty as well as the convergence speed of the constructed MICA model are better than those of other models. The average transaction per second (TPS) value of the proposed IPBFT algorithm in the real digital music copyright protection system is 3469, which is superior to other blockchain technologies. Finally, the digital music copyright protection system is achieved, the error rate of completing the request is 0% in the state of many users operating concurrently, and a high TPS value can be guaranteed. In short, the DCGANs and MICA models pointed out in this study can be used in the composition of monophonic melodies and complex melodies, and the digital music copyright protection system based on the blockchain has excellent performance in practical applications.