Are NFTs Ready to Keep Australian Artists Engaged?

Brian Yecies; Jun Shen; Qin Wang; Ruiqiang Li; Shiping Chen

REVIEW 5 major objections 5 minor 1 cited by

Are NFTs Ready to Keep Australian Artists Engaged?

T0 review · 5 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read An empirical audit of ERC-721 NFTs concludes that current token metadata cannot reliably protect Australian artists' copyrights, with only 203 of 16,769 metadata records containing any copyright or license information.

desk verdict New Australian NFT dataset marred by arithmetic errors and a naive name-matching step that undermines the artist-identification conclusion. read the letter →

arxiv 2507.06926 v1 pith:ZKYXMNUO submitted 2025-07-09 cs.CR cs.CYcs.ET

Ruiqiang Li , Brian Yecies , Qin Wang , Shiping Chen , Jun Shen This is my paper · ORCID

classification cs.CRcs.CYcs.ET

keywords NFTERC-721copyrightmetadataartistidentificationAustralianartistsIndigenousblockchainsecurity

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tests whether NFTs, as currently built on the ERC-721 standard, could serve as a copyright-protection and artist-identification tool for Australian artists, including Indigenous artists. It collected 16,784 successfully retrieved metadata records from Ethereum mainnet NFTs and analyzed copyright, license, security, and artist-name fields. It finds that only about 1.21% of those records contain any copyright or license information, and that artist identification from metadata is unreliable, both because metadata lacks geographic or standardized creator fields and because the author-name list used as ground truth contains errors. The intended conclusion is that the NFT ecosystem is not yet ready to safeguard Australian artists' copyrights or to let the Australian Copyright Agency reliably identify artists on-chain.

What carries the argument

The central object is the ERC-721 NFT metadata record: a JSON document that a token's on-chain tokenURI points to, commonly following OpenSea's template with name, description, image, and attributes fields. The paper's method extracts these records from 43,810 ERC-721 contracts on Ethereum mainnet, decodes on-chain encodings (Base64, UTF-8, ASCII), fetches off-chain metadata from IPFS, Arweave, and cloud platforms, then scans the JSON strings for the keywords 'copyright' and 'license' and for artist names drawn from the Australian Copyright Agency list. This keyword-and-substring scan on a large real-world sample is the mechanism that produces the 1.21% copyright figure and the 199-artist identification list, so the whole argument stands or falls on whether this metadata, and the name list, faithfully represent what artists put into NFTs.

What would settle it

A definitive check would be to re-run the identification pipeline on the same 13,462 metadata records with a cleaned artist register that excludes non-name tokens and includes known pseudonyms; if the corrected match rate is high, the paper's 'very hard to identify' conclusion fails. A second check is to sample newly minted ERC-721 tokens after the introduction of a standardized license field: if a large majority of fresh records carry license data, the 1.21% figure is a snapshot of an early template, not a property of NFTs.

Watch

Extended reading notes

Core claim

The paper claims that NFTs are not ready to protect Australian and Indigenous artists' copyright. The evidence is empirical: of 16,769 acquired metadata records from ERC-721 tokens, only 203 (1.21%) contain copyright or license information, and those records scatter the information across four different metadata locations, with no standard placement. A keyword search for artist names from the Australian Copyright Agency's list found 199 matching tokens in 13,462 correct metadata records, but substring matching produced false positives such as 'What' and 'ME', and the authors themselves note that the name list is incomplete and contains incorrect entries. The paper further reports security issues: missing metadata pointers, inaccessible artwork URLs, and tampering risks in centralized and decentralized storage, while on-chain storage resists tampering but is rarely used. The authors conclude that current NFTs have significant limitations for copyright protection and that identifying Australian artists from metadata is 'very hard.'

Load-bearing premise

The paper's artist-identification result depends on treating the Australian Copyright Agency's name list, which contains non-name entries like 'What' and 'ME', as the ground-truth register of Australian artists.

Editorial extensions

If this is right

If NFTs are to protect Australian artists, metadata standards must require machine-readable copyright and license fields in a fixed location.
Artists and platforms need to include geographic or nationality data and canonical creator names in metadata to make identification feasible.
On-chain storage or hashed pointers are needed to prevent metadata and artwork tampering.
Copyright agencies cannot currently rely on NFT metadata for artist identification; doing so would misattribute works.
Future standards should separate copyright ownership from license terms and follow structured formats like OpenSea attributes.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

This reading extends the paper: a clean test of its conclusion would be to run the same identification pipeline against a curated, deduplicated artist registry rather than the Australian Copyright Agency list; if identification rates rise sharply, the bottleneck is the registry, not NFT metadata.
The paper's low copyright rate may partly reflect OpenSea's early metadata template, which had no dedicated copyright field; platform-driven template changes could quickly shift the statistics without changing the underlying blockchain.
The findings suggest a cost-geometry argument: storing full copyright declarations on-chain is expensive, so off-chain standardized metadata with on-chain hashes is the cheapest path to the protection the paper wants.
For Indigenous artists, the absence of protocols for communal or cultural ownership in ERC-721 metadata is a deeper gap than the paper's counting exercise shows.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The paper is a mixed bag. The central claim—that current ERC-721 NFTs are not ready to serve as a copyright protection tool for Australian and Indigenous artists—is likely true and consistent with what we already know about NFT metadata fragility. The new dataset and the Australian case study are genuinely useful. But the quantitative evidence is not reliable enough to carry the argument.

The strongest part is the empirical collection itself: 43,810 ERC-721 contracts identified from a local Erigon node, with metadata and artwork storage classified into on-chain, IPFS/Arweave, and centralized services. The finding that only about 1.2% of metadata contains copyright or license terms is the most valuable takeaway. The security discussion, while not novel, is well grounded in the storage breakdown.

Unfortunately, there are serious internal inconsistencies. The paper reports 13,462 "correct metadata" in Section 4, then 16,784, then 16,769 in Section 5.1. Duplication counts in Figure 4 sum to 746 tokens, but the text claims 1,159 duplicated metadata. These aren't minor typos; they undermine the denominator for every percentage in the paper.

The artist identification analysis is the weakest pillar. The method is a naive substring search of names from the Australian Copyright Agency register against metadata strings. The register itself contains non-names like "What" and "ME", which account for 113 of the 199 reported matches. The authors note this but still treat the raw results as evidence that identification is "very hard." Worse, the matching algorithm creates systematic false negatives: a full name like "Attafuah, Serwah Ama Gyekyewah Bianca" will never match "Serwah Attafuah" in the metadata. So RQ3's conclusion conflates algorithm failure with absence of identifying information. That's a real problem.

The citation pattern is fine. Self-citations appear for background, but the empirical results don't depend on prior theories. The informal tone in places ("empirically" misspelled in the abstract, duplicated sentences in Section 5.1) suggests the paper was rushed.

Who is this for? Policy researchers and practitioners in the Australian art world who want a quick look at NFT metadata. It could also be a cautionary example for empirical blockchain studies. But with the current numerical inconsistencies and the flawed matching, I would not cite the numbers as reliable. A serious referee should ask for corrected counts, a cleaned name list, and either a more sophisticated matching method (e.g., token-based fuzzy matching) or an explicit statement that the identification result is only a lower bound.

Recommendation: send it to peer review, but expect the reviewers to require substantial revision before any of its quantitative claims are usable.

Referee Report

5 major / 5 minor

Summary. This paper empirically studies ERC-721 NFT metadata and artwork to assess whether NFTs are ready to protect Australian and Indigenous artists' copyright. The authors collect contract addresses from Ethereum blocks 16,000,000 to 21,269,338, identify 43,810 ERC-721 contracts, retrieve and decode metadata from decentralized, centralized, and on-chain storage, and analyze copyright/license keywords, storage distributions, duplication, artwork accessibility, and Australian artist identification by substring matching against the Australian Copyright Agency's name list. They report that only 1.21% of metadata contains copyright or license information, that storage and pointer failures create security risks, and that artist identification is very hard, concluding that NFTs are not ready for this purpose.

Significance. The paper addresses a timely and policy-relevant question, and its empirical approach—using a self-built Erigon node, collecting real on-chain data, and separating metadata and artwork storage types—is a strength that could inform standards discussions. The concrete quantitative findings, such as the low prevalence of license terms and the concentration of storage on IPFS, are potentially useful if the data are cleaned and reported consistently. However, the manuscript as written contains several load-bearing data inconsistencies and a flawed artist-identification benchmark, so the strength of the conclusion is not yet supported.

major comments (5)

[§5.3, Table 2] The RQ3 conclusion that 'it is very hard to identify artists from NFT metadata' is not established by the reported experiment. The top matches in Table 2 come from entries in the Australian Copyright Agency list that are not artist names ('What', 'ME', 'Pcd', 'Ipg', 'Mama'), accounting for 123 of the 199 reported matches, and the substring matcher cannot handle name order or intervening names, as the paper's own example of 'Attafuah, Serwah Ama Gyekyewah Bianca' versus 'Serwah Attafuah' demonstrates. Section 5.3 mentions these issues but treats them as properties of the data rather than as artifacts of the matching procedure and the dirty ground-truth list, so the answer to RQ3 conflates matcher failure with missing metadata.
[§4, Figure 4] The duplication statistics do not add up. The text reports 1,159 duplicated metadata, but the bar values in Figure 4 sum to 746, and 14,466 non-duplicated plus 1,159 duplicated equals 15,625, not the stated 16,784 acquired metadata. The authors should reconcile these numbers or correct them.
[§4 and §5.1] The denominator used for the central 1.21% claim is inconsistent across the paper. Section 4 reports 16,784 acquired metadata and 13,462 correct metadata, while Section 5.1 uses 16,769; the artist identification is performed on 13,462. The authors need to define 'correct metadata' and state exactly which denominator supports each percentage.
[Table 1] The 'Common Terms(%)' column in Table 1 sums to 115%, not 100%, which makes the reported license-type distribution unreliable. The authors should recompute or relabel these values.
[§3 and §5.1] The copyright/license filtering step is under-specified. The text says that after searching for 'copyright' and 'license', some metadata 'has one keyword but does not have any copyright or license information' and is removed, but no criterion is given for making that determination. Since the set of 203 NFTs is the basis for the paper's main quantitative conclusion, this step needs a precise, reproducible definition.

minor comments (5)

[Abstract, §3] There are several typos, including 'empericially' and 'unfortnately' in the abstract and 'inclcues' in Section 3; these should be corrected.
[Table 2] Table 2 is internally inconsistent: the 'Total numbers' column sums to 198, not 199; the percentage for 'Bandi' is 17.4757% but 18/199 is 9.05%; and the percentages in the table sum to more than 100%.
[§3, §4] The paper alternately refers to 43,810 ERC721 'tokens' and 43,810 contract addresses; the authors should clarify whether the unit is contracts or tokens.
[§5.2, Figure 8] The security discussion cites numbers such as 570 wrong-domain cases in decentralized storage and 574 wrong URLs, but these are not clearly traceable to Figure 8 or any table; the authors should make the mapping between text and figure explicit.
[§4] The phrase 'correct metadata' is used to report 13,462 items, but the paper earlier defines only 'acquired metadata'; a precise definition of 'correct' is needed.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's empirical claims rest on collected metadata and external name-list matching, not on definitions or on the authors' prior work.

full rationale

This is an empirical measurement study rather than a derivation. The central claims — that only a small fraction of NFT metadata contains copyright or license information, that storage is divided among centralized/decentralized/on-chain systems, and that artist identification is currently difficult — are supported by counts from data collected from Ethereum RPC and by matching against an external Australian Copyright Agency list. There are no fitted parameters and no equations whose output is defined by an input assumption. The copyright share (203 out of 16,784 metadata records, reported as 1.21%) is a direct keyword-based count, not a prediction forced by construction. The RQ3 conclusion about artist identification is an operational finding of a simple substring search; even if that method is weak, the paper's own Section 5.3 discusses observed mismatches such as 'Attafuah, Serwah Ama Gyekyewah Bianca' versus 'Serwah Attafuah' as reasons for difficulty. That is a measurement-validity limitation, not circularity: the paper does not define 'hard to identify' as 'not found by our exact matcher', and the underlying metadata content is independently observable. Self-citations [7], [13], [14], and [16] are used as background on NFT structure, referable NFT schemes, and metadata-tampering risks; they are not load-bearing for the empirical results, which are externally checkable from blockchain data and the external name registry. There is no imported uniqueness theorem and no ansatz smuggled in via citation. Internal inconsistencies (16,769 vs 16,784 metadata counts; Table 2 summing to 198 instead of 199; percentages that exceed 100%) are correctness/data-quality errors, not signs of circularity.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The study is empirical and introduces no new theoretical entities. Its conclusions depend on dataset choices: the sampled block range, the keyword search terms for copyright and license, and the reliability of the external artist name list.

free parameters (1)

sampled block range = Blocks 16,000,000 to 21,269,338
The Ethereum block range is chosen by the authors without a power analysis or justification that it represents NFTs relevant to Australian artists. All percentages are computed from this convenience sample.

assumptions (4)

domain assumption ERC-721 metadata follows the OpenSea JSON schema with fields such as name, description, image, and attributes.
The extraction of image URLs and copyright fields assumes this schema; Section 3 and the OpenSea link.
domain assumption The Australian Copyright Agency artist name list is a valid ground truth for identifying Australian artists.
Used in Section 4 to detect 199 artists, but the list contains non-name entries such as 'What' and 'ME'.
domain assumption Substring matching of metadata against the name list is a sound method for artist identification.
The paper uses string search over JSON converted to text without accounting for nicknames, pseudonyms, or false positives.
domain assumption The sampled Ethereum blocks and the subset of contracts with accessible metadata are representative of the NFT ecosystem for Australian art.
The paper generalizes from roughly 13,000 usable metadata records out of 768,000 contracts to a verdict on NFTs as a whole.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Are NFTs Ready to Keep Australian Artists Engaged?." pith.science (2026). https://pith.science/paper/ZKYXMNUO

@misc{pith2026250706926,
  author       = {Pith},
  title        = {Pith review of: Are NFTs Ready to Keep Australian Artists Engaged?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZKYXMNUO}},
  note         = {Machine review of arXiv:2507.06926}
}

read the original abstract

Non-Fungible Tokens (NFTs) offer a promising mechanism to protect Australian and Indigenous artists' copyright. They represent and transfer the value of artwork in digital form. Before adopting NFTs to protect Australian artwork, we in this paper investigate them empericially. We focus on examining the details of NFT structure. We start from the underlying structure of NFTs to show how they represent copyright for both artists and production owners, as well as how they aim to safeguard or secure the value of digital artworks. We then involve data collection from various types of sources with different storage methods, including on-chain, centralized, and decentralized systems. Based on both metadata and artwork content, we present our analysis and discussion on the following key issues: copyright, security and artist identification. The final results of the evaluation, unfortnately, show that the NFT is NOT ready to protect Australian and Indigenous artists' copyright.

Figures

Figures reproduced from arXiv: 2507.06926 by the authors.

**Figure 1.** Study Map – RQ1. What kinds of copyright issues arise from NFT metadata and artwork? – RQ2. What kinds of security issues arise from NFT metadata and artwork? – RQ3. Is it easy to identify artists from the metadata level? We have the following contributions ( [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

**Figure 2.** NFT Research Focus 2.1 Copyright Recent research on NFTs has increasingly focused on copyright protection, ownership verification, and secure transactions. Wang et al. propose referable NFTs (rNFTs) using a DAG structure to enhance visibility and profit-sharing among creators [14, 16]. Kimura et al. address risks from blockchain forks with a crossreferencing scheme to preserve NFT uniqueness and platform linkage [… view at source ↗

**Figure 3.** NFT Metadata Details 7 Australian Copyright Agency: https://www.copyright.com.au 7 [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: NFT Metadata Duplication Metadata duplication ( [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

**Figure 5.** Figure 5: NFT Artwork Details Other 16,586 metadata has image URLs. However, some images cannot be collected successfully due to being inaccessible, the wrong domain name or service, and the wrong URL. 13,741 artworks can be collected from metadata. ‘Not accessible’ means that …

**Figure 6.** Figure 6: NFT License and Copyright Positions CC0 18.99% CBE-CC0 1.27% CC BY-NC-ND 4.0 45.57% CC-BY-4.0 1.27% CC BY-NC-SA 4.0 5.06% CC BY-NC 4.0 24.05% CC BY NA 2.0 2.53% CC BY-NC 2.0 1.27% [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]

**Figure 8.** Figure 8: NFT Metadata and Artwork Details In terms of security, addressing metadata and artwork pointer vulnerabilities should be a priority for future research. Decentralized storage systems face notable challenges, such as tampering the risks caused by missing or incorrect p…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Understanding NFTs from EIP Standards
cs.CR 2025-08 conditional novelty 6.0 of 10

A corpus-level study of 191 NFT-related EIPs finds a Draft/Final-bifurcated ecosystem centered on ERC-721/ERC-1155 inheritance, heavy reuse of core functions, skewed contributor participation, and security risk growin...

Reference graph

Works this paper leans on

16 extracted references · 14 canonical work pages · cited by 1 Pith paper

[1]

AACHWA: Pricing and the Art Market.https://aachwa.com.au/wp-content/u ploads/2024/11/AACHWA_Pricing_2022-WEB.pdf (2023), version 1

work page 2023
[2]

Procedia-Social and Behavioral Sciences90, 405–410 (2013)

Abidin, M.Z., Daud, W.S.A.W.M., Rathi, M.R.M.: Printmaking: Understanding the terminology. Procedia-Social and Behavioral Sciences90, 405–410 (2013)

work page 2013
[3]

In: ACM SIGSAC Conference on Computer and Commu- nications Security (CCS) (2022)

Das, D., Bose, P., Ruaro, N., Kruegel, C., Vigna, G.: Understanding security issues in the NFT ecosystem. In: ACM SIGSAC Conference on Computer and Commu- nications Security (CCS) (2022)

work page 2022
[4]

In: IEEE International Conference on Blockchain (Blockchain) (2021)

Igarashi, T., Kazuhiko, T., Kobayashi, Y., Kuno, H., Diehl, E.: Photrace: A blockchain-based traceability system for photographs on the internet. In: IEEE International Conference on Blockchain (Blockchain) (2021)

work page 2021
[5]

In: IEEE International Conference on Blockchain and Cryptocurrency (ICBC) (2023)

Kimura, K., Imamura, M., Omote, K.: Cross-referencing scheme to ensure nft and platform linkage unaffected by forking. In: IEEE International Conference on Blockchain and Cryptocurrency (ICBC) (2023)

work page 2023
[6]

In: IEEE International Conference on Blockchain and Cryptocurrency (ICBC) (2024)

Kotzer, A., Naamneh, M., Rottenstreich, O., Reviriego, P.: Detection of NFT duplications with image hash functions. In: IEEE International Conference on Blockchain and Cryptocurrency (ICBC) (2024)

work page 2024
[7]

arXiv preprint arXiv:2409.11790 (2024)

Li, R., Yecies, B., Wang, Q., Chen, S., Shen, J.: Empowering visual artists with tokenized digital assets with nfts. arXiv preprint arXiv:2409.11790 (2024)

arXiv 2024
[8]

In: International Conference on Blockchain Technology and Information Security (ICBCTIS) (2024)

Liu, J., Lu, S., Wang, Y., Feng, P., Cao, W.: Privacy protection scheme for storage and transaction of non-fungible tokens. In: International Conference on Blockchain Technology and Information Security (ICBCTIS) (2024)

work page 2024

Show all 16 references

[9]

IEEE Transactions on Dependable and Secure Computing (TDSC) (2025)

Ma, Z., Jiang, M., Luo, X., Wang, H., Zhou, Y.: Uncovering NFT domain-specific defects on smart contract bytecode. IEEE Transactions on Dependable and Secure Computing (TDSC) (2025)

2025
[10]

In: IEEE International Conference on Blockchain and Cryptocurrency (ICBC) (2024)

Maesa, D.D.F., Donini, F., Mori, P., Ricci, L.: Protecting non fungible mutable tokens: an application in the metaverse. In: IEEE International Conference on Blockchain and Cryptocurrency (ICBC) (2024)

2024
[11]

In: Interna- tional Conference on Electrical and Information Technology (IEIT) (2022)

Mochram, R.A.A., Makawowor, C.T., Tanujaya, K.M., Moniaga, J.V., Jabar, B.A.: Systematic literature review: blockchain security in NFT ownership. In: Interna- tional Conference on Electrical and Information Technology (IEIT) (2022)

2022
[12]

In: International Conference on Financial Cryp- tography and Data Security (FC) (2023)

Stöger, F., Zhou, A., Duan, H., Perrig, A.: Demystifying Web3 centralization: the case of off-chain NFT hijacking. In: International Conference on Financial Cryp- tography and Data Security (FC) (2023)

2023
[13]

arXiv preprint arXiv:2105.07447 (2021)

Wang, Q., Li, R., Wang, Q., Chen, S.: Non-fungible token (nft): Overview, evalu- ation, opportunities and challenges. arXiv preprint arXiv:2105.07447 (2021)

2021 arXiv
[14]

In: IEEE International Conference on Blockchain and Cryptocurrency (ICBC) (2023)

Wang, Q., Yu, G., Fu, S., Chen, S., Yu, J., Xu, X.: A referable NFT scheme. In: IEEE International Conference on Blockchain and Cryptocurrency (ICBC) (2023)

2023
[15]

In: Proceedings of the ACM Web Conference (WWW) (2023)

Wang, Z., Gao, J., Wei, X.: Do NFTs’ owners really possess their assets? a first look at the nft-to-asset connection fragility. In: Proceedings of the ACM Web Conference (WWW) (2023)

2023
[16]

IEEE Transactions on Services Com- puting (TSC) (2025) 15

Yu, G., Wang, Q., Sun, C., Nguyen, L.D., Bandara, H.D., Chen, S.: Maximizing NFT incentives: References make you rich. IEEE Transactions on Services Com- puting (TSC) (2025) 15

2025

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

AACHWA: Pricing and the Art Market.https://aachwa.com.au/wp-content/u ploads/2024/11/AACHWA_Pricing_2022-WEB.pdf (2023), version 1

work page 2023

[2] [2]

Procedia-Social and Behavioral Sciences90, 405–410 (2013)

Abidin, M.Z., Daud, W.S.A.W.M., Rathi, M.R.M.: Printmaking: Understanding the terminology. Procedia-Social and Behavioral Sciences90, 405–410 (2013)

work page 2013

[3] [3]

In: ACM SIGSAC Conference on Computer and Commu- nications Security (CCS) (2022)

Das, D., Bose, P., Ruaro, N., Kruegel, C., Vigna, G.: Understanding security issues in the NFT ecosystem. In: ACM SIGSAC Conference on Computer and Commu- nications Security (CCS) (2022)

work page 2022

[4] [4]

In: IEEE International Conference on Blockchain (Blockchain) (2021)

Igarashi, T., Kazuhiko, T., Kobayashi, Y., Kuno, H., Diehl, E.: Photrace: A blockchain-based traceability system for photographs on the internet. In: IEEE International Conference on Blockchain (Blockchain) (2021)

work page 2021

[5] [5]

In: IEEE International Conference on Blockchain and Cryptocurrency (ICBC) (2023)

Kimura, K., Imamura, M., Omote, K.: Cross-referencing scheme to ensure nft and platform linkage unaffected by forking. In: IEEE International Conference on Blockchain and Cryptocurrency (ICBC) (2023)

work page 2023

[6] [6]

In: IEEE International Conference on Blockchain and Cryptocurrency (ICBC) (2024)

Kotzer, A., Naamneh, M., Rottenstreich, O., Reviriego, P.: Detection of NFT duplications with image hash functions. In: IEEE International Conference on Blockchain and Cryptocurrency (ICBC) (2024)

work page 2024

[7] [7]

arXiv preprint arXiv:2409.11790 (2024)

Li, R., Yecies, B., Wang, Q., Chen, S., Shen, J.: Empowering visual artists with tokenized digital assets with nfts. arXiv preprint arXiv:2409.11790 (2024)

arXiv 2024

[8] [8]

In: International Conference on Blockchain Technology and Information Security (ICBCTIS) (2024)

Liu, J., Lu, S., Wang, Y., Feng, P., Cao, W.: Privacy protection scheme for storage and transaction of non-fungible tokens. In: International Conference on Blockchain Technology and Information Security (ICBCTIS) (2024)

work page 2024