[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125990-en":3,"doc-seo-125990-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125990,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","NFT Cryptopunk Generation Using Machine Learning Algorithm (DCGAN) - A Deep Learning Approach","A non-fungible token (NFT) represents ownership or authenticity for unique digital assets such as artwork, music, and media. This study explores using a deep convolutional generative adversarial network (DCGAN) to generate original Cryptopunk images that can be converted into NFTs. A model is trained on an existing Cryptopunks dataset, while multiple hyperparameters and layer combinations are tested to improve image quality. Results report a 15% higher inception score and a 20% lower Fréchet inception distance, indicating more distinctive and higher-quality outputs.","[https://doi.org/10.5755/j02.eie.38435](https://doi.org/10.5755/j02.eie.38435)  \nNFT Cryptopunk Generation Using Machine Learning Algorithm (DCGAN)  \nPooja Singhal1, Deepak Aneja1, Musaed Alhussein2, Ritu Gupta3, Khursheed Aurangzeb2, Nitish Pathak3,*  \n1Department of Computer Science, ABES Engineering College,  \nGhaziabad, UP, India  \n2Department of Computer Engineering, College of Computer and Information Sciences, King Saud University,  \nP. O. Box 51178, Riyadh 11543, Saudi Arabia  \n3Bhagwan Parshuram Institute of Technology, GGSIPU,  \nNew Delhi, India  \n[pooja.singhal@abes.ac.in](pooja.singhal@abes.ac.in); [deepak.aneja@abes.ac.in](deepak.aneja@abes.ac.in); [musaed@ccis.ksu.edu.sa](musaed@ccis.ksu.edu.sa); [ritu4006@gmail.com](ritu4006@gmail.com);  \n[kaurangzeb@ksu.edu.sa](kaurangzeb@ksu.edu.sa);*[nitishpathak@bpitindia.com](nitishpathak@bpitindia.com)  \nAbstract—A non-fungible token (NFT) is a kind of digital asset that signifies ownership or proof of authenticity of a special good or piece of material, such as artwork, music, films, or tweets. This study investigates how a deep convolutional generative adversarial network (DCGAN) can be used to create distinctive pictures of Cryptopunks that can be converted into NFTs. Cryptopunks, a pioneering form of NFTs, were introduced on the Ethereum blockchain in 2017 as part of asocial experiment. In the NFT community, they have since grown in popularity as collectibles. To create brand-new, previously undiscovered characters, we trained a model on adataset of existing Cryptopunks using the DCGAN architecture. In an effort to raise the calibre of the images produced, we tested various hyper settings and layer combinations. We also assessed the created images using a variety of criteria, such as the inception score and Fréchet inception distance, to make sure they were distinctive and of high calibre. Our experiments yielded a 15 % increase in the inception score and a 20 % decrease in the Fréchet inception distance, showing that our DCGAN model produces images that are more visually appealing and closer in quality to real Cryptopunks. These results highlight the effectiveness of our machine learning algorithms in improving the quality and uniqueness of NFT assets.  \nIndex Terms—Blockchain; Cryptopunks; DCGAN; Nonfungible token (NFT).  \nI. INTRODUCTION  \nBlockchains are distributed digital ledgers that are used to track transactions among numerous computers. It employs encryption to protect and validate transactions, as well as to regulate the production of new units of a certain cryptocurrency. Each block in the chain has a number of transactions and a link to the preceding block that connects them all. This results in the creation of an unalterable permanent record of every network transaction. Although the blockchain that underpins Bitcoin’s cryptocurrency is the most well-known example, there are many additional applications for this technology as well [1] .  \nManuscript received 9 June, 2024; accepted 15 September, 2024. This research is funded by the Researchers Supporting Project under Grant No. RSPD2024R553, King Saud University, Riyadh, Saudi Arabia.  \nNFT Cryptopunks are a set of unique digital characters created by Larva Labs in 2017 on the Ethereum blockchain. Each Cryptopunk has a distinct combination of characteristics, including different hairstyles, accessories, and facial expressions. Although certain Cryptopunks are more uncommon than others, each one is regarded as a unique digital collectable. Contrary to cryptocurrencies such as Bitcoin, which are fungible and interchangeable, nonfungible tokens (NFTs) refer to a class of digital assets that are singular and indivisible [2] . Since each Cryptopunk is an NFT, its ownership and authenticity can be established using its specific blockchain identification. Since their inception, Cryptopunks have grown in popularity and are now among the most expensive NFT artefacts, with some going for millions of dollars at aucti","cbCairOhxRl3ZGRo","https://ap.wps.com/l/cbCairOhxRl3ZGRo","pdf",581112,4,1,9,"English","en",105,"# Introduction\n## Technical Components\n# Literature Review\n# Methodology and Workflow\n# Results and Discussion\n# Conclusion and Future Scope","[{\"question\":\"How does the DCGAN model generate new Cryptopunk images for NFTs?\",\"answer\":\"The DCGAN is trained on an existing Cryptopunks dataset to learn distinctive visual features. After training, it produces brand-new, previously undiscovered character images that can be used as NFT assets.\"},{\"question\":\"What hyperparameters and model combinations were evaluated in the study?\",\"answer\":\"The study tests various hyper settings and layer combinations with the goal of improving the calibre of generated images. The best configurations are selected based on evaluation metrics.\"},{\"question\":\"Which evaluation metrics were used to measure image quality and distinctiveness?\",\"answer\":\"The generated images are assessed using metrics including the inception score and Fréchet inception distance (FID), ensuring outputs are distinctive and of high quality.\"}]","NFT Cryptopunk Generation Using Machine Learning Algorithm (DCGAN) - A Deep Learning Approach | PDF",1785902430,23,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"nft-cryptopunk-generation-using-machine-learning-algorithm-dcgan-a-deep-learning-approach","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/nft-cryptopunk-generation-using-machine-learning-algorithm-dcgan-a-deep-learning-approach/125990/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"How does the DCGAN model generate new Cryptopunk images for NFTs?","Question",{"text":76,"@type":77},"The DCGAN is trained on an existing Cryptopunks dataset to learn distinctive visual features. After training, it produces brand-new, previously undiscovered character images that can be used as NFT assets.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What hyperparameters and model combinations were evaluated in the study?",{"text":81,"@type":77},"The study tests various hyper settings and layer combinations with the goal of improving the calibre of generated images. The best configurations are selected based on evaluation metrics.",{"name":83,"@type":74,"acceptedAnswer":84},"Which evaluation metrics were used to measure image quality and distinctiveness?",{"text":85,"@type":77},"The generated images are assessed using metrics including the inception score and Fréchet inception distance (FID), ensuring outputs are distinctive and of high quality.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]