# NFT Image Plagiarism Check Using EfficientNet-Based Deep Neural Network with Triplet Semi-Hard Loss
## 2 System Overview
### Proposed Solution Scheme  
The proposed design architecture for the plagiarism detector on the NFT system is depicted in Figure 1. It is based on the lack of a built-in method on the blockchain to confirm that the person minting an NFT has the right to the asset they are minting [17]. Therefore, we propose a system to identify plagiarized NFT images as an AI model placed before the minting process to verify that only original and legally obtained images are being minted as NFTs. We employ an AI model to check for plagiarism before minting, which is expected to reduce computing costs because the model only needs to be run once for each image. Additionally, it can decrease the need for numerous copies of the same image to be stored, which can save cost and storage space. This study analyzes and compares the processing times for all models mentioned earlier. This paper outlines five requirements for using blockchain technology in the NFT system. First, choosing the right blockchain platform, such as Ethereum, due to its scalability, security, and transaction speed [18]. Second, developing a smart contract with a token can be assigned to each individual work of art. Thus, the owner of the NFT has blockchain-based evidence of the asset's authenticity. This is crucial because there have been instances in the past where individuals have attempted to manipulate already-sold NFTs [19]. Third, building interoperability to ensure that the NFTs created on the blockchain are interoperable, which refers to the ability to exchange data between different blockchain platforms and networks [20]. Fourth, deploying IPFS to cover storage and security, since we need to store the NFT image securely and immutably on the blockchain [21][22]. Due to our system being intended to allow NFT-based art, it is crucial to take into account the supporting technologies. The usage of IPFS in our study indicates how we took the NFT system into account and worked to address the issue of NFT picture plagiarism as a whole. IPFS can be used as a decentralized storage system that ensures the NFTs cannot be altered or deleted by anybody, including the creator of the image [23]. Fifth, integrating with AI, which is necessary to ensure that the AI model for plagiarism checking is appropriately integrated with the blockchain platform so that it can interact with the NFTs and verify their originality. The proposed system has two flows: one from creators who upload NFT images, represented by the red line, and one from users who download or purchase NFT images, represented by the blue line, as shown in Figure 1. The proposed solution addresses the issues of combining all anti-plagiarism-related tasks into a single procedure and ensuring security and privacy protection. A decentralized app with all the capabilities and services, including anti-plagiarism and advanced application programming interface (API), is included in the proposed solution. One of the best decentralized options for storing NFT files is IPFS (InterPlanetary File System), a distributed system for uploading, storing, and accessing websites, programs, data, and files. IPFS offers a decentralized way to host and access content as a P2P file-sharing protocol, with user operators hosting a portion of the total data, resulting in a unique and cutting-edge system for storing and distributing files or other content. Unlike the conventional hypertext transfer protocol (HTTP) protocol, IPFS uses a content-addressing method. Each content within the IPFS ecosystem has a unique hash that acts as a content identification (CID). As a result, users of IPFS can find any file, website, and other data by searching for the relevant cryptographic hash instead of searching by location [24]. We have developed a novel strategy that uses current computer vision and deep learning developments, including the EfficientNet-based model with the triple semi-hard loss function. Although our system does not employ specific NFT-related features, it can be implemented in any NFT system or marketplace.