# NFT Image Plagiarism Check Using EfficientNet-Based Deep Neural Network with Triplet Semi-Hard Loss
## 1 Introduction  
Blockchain technology is avant-garde for digital entities. NFTs allow for the open transfer of ownership and the provable scarcity of digital goods. These new characteristics have enormous power for innovators. They can sell one-of-a-kind and authenticated things on a blockchain-based marketplace rather than spreading their works of art, such as images, music, or other creations, on platforms that are typically difficult to monetize. By relying on the irrefutable blockchain history of NFTs rather than the reseller's word, consumers may be confident in the legitimacy of any digital commodity they purchase [1]. An NFT is a digital asset that cannot be exchanged for another NFT as each token has a unique value, confirmed through data sets such as blockchain, token ID, token type, metadata, and contact address. NFTs can take the form of rare and unique images, movies, video game items, and artistic works [2]. NFTs controlled by a blockchain network indicate ownership of physical or digital items. The Ethereum network is currently one of the most widely utilized blockchain networks. On this network, people can deploy smart contracts and mint NFTs, allowing them to run Ethereum network apps. Since this network has been supported and regulated by a community, they do not need any devotion in any particular person to utilize it, provided the community has enough members. [3]. Although the highlighted advantages of NFT, NFT forgery and counterfeiting [4] have increasingly become a problem in the industry. For example, more than 80% of the NFTs are found in OpenSea, one of the NFT marketplaces. These recently made through its free minting tool were either spam, fake supplies, or plagiarized creations [5]. To overcome thisissue, this paper proposes a method to detect plagiarism in NFT marketplaces by enhancing deep neural networks. Few research articles describe how to check NFT images for plagiarism. [PERSON] et al. [6] have developed a technique for approximation design matching for NFT image plagiarism detection, with special relevance to blockchain-driven NFT platforms and ecosystems. The authors used a non-deterministic finite automaton (NDFA) approach in conjunction with a sliding window idea and local thresholds at the node level to trace partial matches. This technique is comparable to other similarity measures currently used in text mining for plagiarism detection. On the other hand, numerous methods have been developed to identify images that have been plagiarized, such as [PERSON] et al. [7] who suggested an improved approach to identify image plagiarism using RGB (red, green, and blue) and HSV (hue, saturation, and value) color spaces, using Tamura texture, and the clever edge technique for shape to retrieve features from photos and save to databases. The results were displayed with true and false statements and similarity indices in ascending order. However, the precision of the modified images is low grade. [PERSON] et al. [8] have proposed a plagiarism detection method based on GAN and employ CNN, primarily used in the preprocessing stage. In this method, the dataset is first analyzed by the GAN, and then its output is fed into the CNN. The model will then indicate whether or not a GAN created the image. Nevertheless, the authors should have explained how they achieved the results in more detail, and there was no comparison to other methods. Furthermore, deep convolutional neural networks, such as those used by [PERSON] et al. [9], can recognize several types of image similarity observed in academic work and may identify plagiarism in images. The adaptability of the technique is achieved by incorporating approaches for assessing various image features, selectively deploying analysis techniques based on how well they complete the input image and applying an adaptable strategy for recognizing suspicious image similarities. However, images resembling one another do not appear as suspicious outliers in noticeably bigger data sets.