In recent times, with the rapid proliferation of the metaverse environment, there has been a notable upswing in instances of performance within virtual realms, capitalizing on the inherent characteristics of this environment. These performances, conducted within virtual settings, are commonly referred to as virtual performances. The distinctive advantage of these virtual performances lies in their unrestricted accessibility, transcending the spatial confines of the physical world. As such, they are emerging as a viable substitution for overseas concerts that might be impeded by spatial constraints brought about by infectious disease pandemics or geographical remoteness. These virtual performances integrate a myriad of digital components, termed digital assets, to coalesce into a singular content. Consequently, the inherent trait of digital data, characterized by its ease of duplication and reuse, can be directly leveraged. Nevertheless, there is a concurrent rise in instances of copyright infringement, stemming from the unauthorized replication, reuse, and manipulation of these features. In this study, we propose technologies centered around redundancy and similarity determination for digital data, aiming to address these issues. This technology is employed to ascertain whether a given dataset corresponds to a previously registered digital asset or if it has undergone partial modifications when an author endeavors to register a new digital asset within the digital asset market.
Through this, this study blocks digital asset data, divides it into several pieces, and determines the redundancy and similarity of digital assets by analyzing whether the blocks match existing registered digital assets.
Through this, it is possible to prevent unauthorized registration of digital assets that have been copied or modified by rights infringers in advance.