D-ARTNET22 V1: a neural network framework against stolen digital artworks getting Non-Fungible Token (NFT) labels
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
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| Lenguaje: | English |
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Brac University
2024
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| Acceso en línea: | http://hdl.handle.net/10361/23617 |
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10361-236172024-06-27T21:04:50Z D-ARTNET22 V1: a neural network framework against stolen digital artworks getting Non-Fungible Token (NFT) labels Dipro, Farhan Hasin Simran, Sheikh Saif Akhter, Mansura Momo, Ramisa Fariha Musharrat, Ramisa Hossain, Muhammad Iqbal Rodoshi, Ahanaf Hassan Department of Computer Science and Engineering, Brac University Non-Fungible Tokens (NFT) Digital art Convolutional Neural Net- works (CNN) You Only Look Once (YOLO) Adobe Photoshop CC Image forgery Image classification Object detection Neural networks (Computer science) Image processing--Digital techniques. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 54-56). "Since NFT sites gained fame for selling digital arts, NFT crimes have taken a toll on excessive amounts of digital content creators as stolen digital artworks get their ownership changed permanently in the name of the thief, further getting sold on humongous fortunes. Due to NFT sites not having any user or content verification system before registration, thieves tend to take the chance of scamming even more by adopting various forgery protocols. Artworks from social media or NFT sites are stolen, forged, and then registered under different names. On the contrary, since blockchains are immutable, the thief remains the owner of the stolen NFT forever which implies that NFT sites fail to provide a secure space for hardworking digital content creators. According to what has been researched, it is discovered that there exists no such work relating to digital media. Despite connecting some certain dis- joint fields, the results were not promising and thus they were not thought to be implemented in real life. Besides, digital artwork datasets are not available online for the purpose of this field to be served. A possible methodology can be doing exten- sive image scraping on selective digital media platforms to extract digital artworks that may then be modified to create a fabricated artwork dataset. This dataset can subsequently be used to train deep learning or neural network models to distinguish between actual and false entities. As no verification system for NFT sites has been proposed before, it is crucial to develop a system to check the authentication of dig- ital artworks and the user before the NFT transaction is passed into the blockchain. Therefore, for the very first time, this paper will present a framework that will check the originality of digital artworks before accepting them as an NFT permanently." Farhan Hasin Dipro Sheikh Saif Simran Mansura Akhter Ramisa Fariha Momo Ramisa Musharrat B.Sc in Computer Science 2024-06-27T04:28:52Z 2024-06-27T04:28:52Z 2022 2022-05 Thesis ID 18101627 ID 18201189 ID 18301031 ID 18301034 ID 18301233 http://hdl.handle.net/10361/23617 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 56 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Non-Fungible Tokens (NFT) Digital art Convolutional Neural Net- works (CNN) You Only Look Once (YOLO) Adobe Photoshop CC Image forgery Image classification Object detection Neural networks (Computer science) Image processing--Digital techniques. |
| spellingShingle |
Non-Fungible Tokens (NFT) Digital art Convolutional Neural Net- works (CNN) You Only Look Once (YOLO) Adobe Photoshop CC Image forgery Image classification Object detection Neural networks (Computer science) Image processing--Digital techniques. Dipro, Farhan Hasin Simran, Sheikh Saif Akhter, Mansura Momo, Ramisa Fariha Musharrat, Ramisa D-ARTNET22 V1: a neural network framework against stolen digital artworks getting Non-Fungible Token (NFT) labels |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. |
| author2 |
Hossain, Muhammad Iqbal |
| author_facet |
Hossain, Muhammad Iqbal Dipro, Farhan Hasin Simran, Sheikh Saif Akhter, Mansura Momo, Ramisa Fariha Musharrat, Ramisa |
| format |
Thesis |
| author |
Dipro, Farhan Hasin Simran, Sheikh Saif Akhter, Mansura Momo, Ramisa Fariha Musharrat, Ramisa |
| author_sort |
Dipro, Farhan Hasin |
| title |
D-ARTNET22 V1: a neural network framework against stolen digital artworks getting Non-Fungible Token (NFT) labels |
| title_short |
D-ARTNET22 V1: a neural network framework against stolen digital artworks getting Non-Fungible Token (NFT) labels |
| title_full |
D-ARTNET22 V1: a neural network framework against stolen digital artworks getting Non-Fungible Token (NFT) labels |
| title_fullStr |
D-ARTNET22 V1: a neural network framework against stolen digital artworks getting Non-Fungible Token (NFT) labels |
| title_full_unstemmed |
D-ARTNET22 V1: a neural network framework against stolen digital artworks getting Non-Fungible Token (NFT) labels |
| title_sort |
d-artnet22 v1: a neural network framework against stolen digital artworks getting non-fungible token (nft) labels |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/23617 |
| work_keys_str_mv |
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