Non-fungible tokens (NFTs) have become a significant digital asset class, each uniquely representing virtual entities such as artworks. These tokens are stored in collections within smart contracts and are actively traded across platforms on Ethereum, Bitcoin, and Solana blockchains. The value of NFTs is closely tied to their distinctive characteristics that define rarity, leading to a growing interest in quantifying rarity within both industry and academia. While there are existing rarity meters for assessing NFT rarity, comparing them can be challenging without direct access to the underlying collection data. The Rating over all Rarities (ROAR) benchmark addresses this challenge by providing a standardized framework for evaluating NFT rarity. This paper explores a dimension reduction approach to rarity design, introducing new performance measures and meters, and evaluates them using the ROAR benchmark. Our contributions to the rarity meter design issue include developing an optimal rarity meter design using non-metric weighted multidimensional scaling, introducing Dissimilarity in Trades (DIT) as a performance measure inspired by dimension reduction techniques, and unveiling the non-interpretable rarity meter DIT, which demonstrates superior performance compared to existing methods.

Recent studies using indirect statistical methods estimate that around 70% of traded value on centralized crypto exchanges like Binance, can be characterized as wash trading. This paper turns to NFT markets, where transaction transparency, including analysis of roundtrip trades and common wallet activities, allows for more accurate direct estimation methods to be applied. We find roughly 30% of NFT volume and between 45-95% of traded value, involve wash trading. More importantly, our approach enables a critical evaluation of common indirect estimation methods used in the literature. We find major differences in their effectiveness; some failing entirely. Roundedness filters, like those used in Cong et al. (2023), emerge as the most accurate. In fact, the two approaches can be closely aligned via hyper-parameter optimization if direct data is available.

In the evolving landscape of digital art, Non-Fungible Tokens (NFTs) have emerged as a groundbreaking platform, bridging the realms of art and technology. NFTs serve as the foundational framework that has revolutionized the market for digital art, enabling artists to showcase and monetize their creations in unprecedented ways. NFTs combine metadata stored on the blockchain with off-chain data, such as images, to create a novel form of digital ownership. It is not fully understood how these factors come together to determine NFT prices. In this study, we analyze both on-chain and off-chain data of NFT collections trading on OpenSea to understand what influences NFT pricing. Our results show that while text and image data of the NFTs can be used to explain price variations within collections, the extracted features do not generalize to new, unseen collections. Furthermore, we find that an NFT collection's trading volume often relates to its online presence, like social media followers and website traffic.

Non-Fungible Tokens (NFTs) are non-interchangeable assets, usually digital art, which are stored on the blockchain. Preliminary studies find that female and darker-skinned NFTs are valued less than their male and lighter-skinned counterparts. However, these studies analyze only the CryptoPunks collection. We test the statistical significance of race and gender biases in the prices of CryptoPunks and present the first study of gender bias in the broader NFT market. We find evidence of racial bias but not gender bias. Our work also introduces a dataset of gender-labeled NFT collections to advance the broader study of social equity in this emerging market.

Non-fungible tokens (NFTs) are digital assets stored on a blockchain representing real-world objects such as art or collectibles. An NFT collection comprises numerous tokens; each token can be transacted multiple times. It is a multibillion-dollar market where the number of collections has more than doubled in 2022. In this paper, we want to obtain a generative model that, given the early transactions history (first quarter Q1) of a newly minted collection, generates subsequent transactions (quarters Q2, Q3, Q4), where the generative model is trained using the transaction history of a few mature collections. The goal is to use the generated transactions to project the potential market value of this newly minted collection over the next few quarters. A technical challenge exists in that different collections have diverse characteristics, and the generative model should generate based on the appropriate "contexts" of the collection. Our method takes a two-step approach. First, it employs unsupervised learning on the early transactions to extract characteristics (which we call contexts) of NFT collections. Next, it generates future transactions of each token based on these contexts and the early transactions, projecting the target collection's potential market value. Comprehensive experiments demonstrate our contextual generative approach's NFT projection capabilities.

Artificial Intelligence (AI) is a big trend right now, but not all of us have access to the data scientists or large budgets needed to use AI technology. Thankfully, there are many free tools out there that you can use without any coding experience at all -- and they're more useful than ever before thanks to the rise of machine learning. Here are seven awesome AI tools that are 100% free to try! Copy.ai is a free online content creation tool that lets you create blog posts, articles, and more. It's easy to use, and it can be used for all kinds of content like blog posts, Instagram captions, YouTube video ideas, and article titles. I am totally obsessed with Copy.ai.

In the new world of virtual existence, the first AI-powered NFT collection was launched by XANA. The collection includes 10,000 original avatars with a Japanese anime aesthetic which would serve as a customised AI ally across the metaverse. The NFT collection is inspired by Blade Runner 2049 to cater to the demand of personalised avatars for the metaverse. Other features that the NFT collection carries are full commercial rights and the power of the AI engine, among several other ecosystem privileges. Here comes the reveal of #XANA Genesis Project The first-ever original AI-based #NFTs of XANA.

The recent growth of the overall NFT market has created big challenges for development teams trying to scale market infrastructure. And as NFT trading volume has increased dramatically, so has the incentive for bad actors to sell plagiarized art and counterfeit NFTs. At the same time, the need to verify the legitimacy of NFT collections has created problems for many NFT creators. Since it takes marketplaces just as much effort (if not more) to verify a small collection as a large one, marketplaces are incentivized to put off verifying smaller collections in favor of larger ones that generate more revenue. And for creators of smaller NFT collections, this incentive frequently leads to lengthy delays in gaining visibility on leading marketplaces. Even in relatively small NFT ecosystems, current verification methods create market bottlenecks that are bad for creators, marketplaces and collectors.

OpenSea is putting in place a new system to spot NFT fakes and verify accounts, in an effort to cut down on the industry's growing fraud problem. In a couple of blog posts, the NFT marketplace detailed what changes users can expect, including opening up verification to more users, automated and human-assisted removal of so-called "copymints" or fake copies of authentic NFTs and changes to how collection badges -- which identify NFT collections with high sales volume or interest -- are doled out on the marketplace. First off, OpenSea will use a two-part system to detect fakes that combine both image recognition tech and human reviewers. The company says its new system will continuously scan all NFT collections (including newly minted assets) to spot any potential fakes. "Our new copymint prevention system leverages computer-vision tech to scan all NFTs on OpenSea (including new mints). The system then matches these scans against a set of authentic collections, starting with some of the most copy-minted collections -- we'll look for flips, rotations & other permutations," wrote OpenSea's Anne Fauvre-Willis in the post.

Rather, it's because people value their scarcity, prospect their future price, appreciate their proof-of-concept of a new artform, or love the artist who created them. However, a new trend in the art world, AI-generated art, may soon disrupt the multibillion-dollar NFT space. First, let's look at how collections of NFTs are created today. In most high-profile NFT projects, artists design multiple classes of varying attributes such as hair color, background color, or skin tone. Then, artists will mix and match these attributes to create a collection of "unique" NFTs, where no two NFTs look the same.