The Development of A Partipatory Art Ecosystem using GenerativeAdversarial Networks and Blockchains

Abstract

Participatory art is a word that refers to a type of art that involves the audience directly in the creative process, making them participants in the event. In this regard, the artist is viewed as a collaborator and co-producer of art with the audience, and these situations frequently have an ambiguous beginning or end. At the end of the 20th century, partic ipatory art was introduced in almost every field of life; science, politics, philosophy, and the arts. The idea of participation was combined in a creative synergy with the devel opment of technology to produce a considerably extended array of types and manners of participation. In this climate of technological change, an increasing number of artists are producing participatory works of art that use interactive computer technology. Our research proposed a pARTicipatoryGAN which is a mix of blockchains and generative adversarial networks. With this tool, we aim to develop a participatory art ecosystem: a matrix for works of art that allows anyone who wants to create participatory art to join the ecosystem. The participants would be the co-authors of the art, therefore, there will also be the element of a sense of belonging with that art, In the ecosystem, partic ipants can also auction that art as an NFT and enjoy the royalties. The thesis focuses on researching whether the Blockchain and GANs can address the problems associated with the traditional participatory system. Hence, we begin by stating our hypothesis that the mix of GANs and Blockchain architectures seems to be a good match for our proposed Participatory Art Ecosystem Development. We gleaned the major requirements that are necessary for our Participatory Art Ecosys tem and the possible information sources from blockchains. Later, we conducted an extensive literature review to pick one out of many existing deep learning GANs that suffices to fulfill most, if not all, of those listed requirements. In the end, we validated our proposed solution to find out the extent to which it could fulfill the listed requirements. From its validation, we were able to draw some conclu sions. The fact is that our design architecture proved to meet the majority of require ments for a participatory art ecosystem, but a few others remain unaddressed. Our aim was to define achievable and unachievable criteria through our proposed solution explicitly and to show the potential of blockchain and generative adversarial networks in the participatory art realm.