# NFT Image Plagiarism Check Using EfficientNet-Based Deep Neural Network with Triplet Semi-Hard Loss
## 5 Conclusions  
Image plagiarism in the NFT system can have a negative impact on both artists and customers; there is a necessity for a framework to provide a security system to secure all of the related parts. Developing a reliable detection of plagiarism system is essential to ensure that people who relate to the NFT system can get protection. We developed and implemented the EfficientNet-B0-Based DNN with T triplet semi-hard loss to build image plagiarism detection in the NFT system in this study. Furthermore, the proposed EfficientNet-B0-Based DNN with triplet semi-hard loss method was implemented for detecting plagiarism NFT images to obtain the lowest loss and the highest accuracy compared with ResNet50, DenseNet, and MobileNetV2 models for various threshold scores of 0.55, 0.65, and 0.75.  
Although we gained promising results, some other studies must be added in the future. This future research needs to expand the dataset to include a more extensive and diverse set of NFT images to improve the model's performance. Furthermore, the proposed method can be enhanced and deployed in real-world scenarios to determine its efficacy in detecting plagiarism in NFT images. The proposed solution might be implemented into current NFT marketplaces such as Opensea, Rarible, SuperRare, etc., adding extra security for artists and customers. The method can be deployed as a preprocessing step before placing NFT images on the market, ensuring that only original images are utilized.  
In addition, the proposed method can be applied to other digital media besides images, such as films and music. As the NFT ecosystem increases, the demand for secure and trustworthy plagiarism detection solutions will also be increased. Implementing the proposed method in several NFT marketplaces might secure artists' rights and increase the credibility and reliability of the entire NFT system.  
Figure 16: Simulation of uploading original NFT image.  
**Author Contributions:** Conceptualization, A.T.P., N.S., and S.O.; methodology, A.T.P. and N.S.; software, A.T.P. and N.S.; validation, A.T.P., N.S., and T.-T.-H.L.; formal analysis, A.T.P.; investigation, A.T.P.; resources, A.T.P. and S.O.; data curation, A.T.P.; writing--original draft preparation, A.T.P.; writing--review and editing, A.T.P. and T.-T.-H.L.; visualization, A.T.P., N.S. and S.O.; supervision, T.-T.-H.L. and H.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the Convergence security core talent training business (Pusan National University) support program (IITP-2023-2022-0-01201) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation) and a part by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2023-2020-0-01797) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data available in a publicly accessible repository that does not issue DOIs. Publicly available datasets were analyzed in this study. This data can be found here: [[https://www.kaggle.com/datasets/shaunmak/nft-classifier](https://www.kaggle.com/datasets/shaunmak/nft-classifier)]([https://www.kaggle.com/datasets/shaunmak/nft-classifier](https://www.kaggle.com/datasets/shaunmak/nft-classifier)) (accessed on 19 November 2022).  
**Conflicts of Interest:** The authors declare no conflict of interest.