PREDICTING DERIVATIVE NFT IMAGES USING CONVOLUTIONAL NEURAL NETWORK WITH THE DENSENET201 MODEL

Rhama Andyka, Yonathan Purbo Santosa

Abstract


Derivative NFTs are modified versions of the original NFTs that have been altered or obtained through additional processing. This modification process may include changes in the color, appearance, composition, or content of an existing digital asset. Penelitian ini bertujuan untuk mengembangkan algoritma prediksi untuk mengklasifikasikan derivatif NFT (Non-Fungible Token) menggunakan teknik deep learning. In this context, the developed algorithm uses the DenseNet-201 architecture and involves steps such as data comprehension, data preparation, image augmentation, and the use of callbacks to stop model training when it reaches the desired level of accuracy. This study uses NFT-derived datasets collected by the researchers themselves, because there is no source that provides a large number of NFT datasets. Through experiments conducted, it is known that the use of DenseNet-201 architecture with a target size of 50x50 or 150x150 can produce a good level of accuracy, reaching 86-99%. The experimental results show that the implemented DenseNet-201 model is capable of classifying NFT derivatives with a good level of accuracy. The use of data augmentation and adjustment of certain hyperparameters also affects the improvement of model accuracy. In addition, analysis and visualization of the results were carried out using a confusion matrix to evaluate the performance of the model in classifying each NFT derived class.


Keywords


Machine Learning; Convolution Neural Network; Transfer learning; DenseNet201 Architecture

Full Text:

PDF

References


Q. Wang, R. Li, Q. Wang, and S. Chen, “Non-Fungible Token (NFT): Overview, Evaluation, Opportunities and Challenges,” May 2021, [Online]. Available: http://arxiv.org/abs/2105.07447

N. Meuschke, C. Gondek, D. Seebacher, C. Breitinger, D. Keim, and B. Gipp, “An Adaptive Image-based Plagiarism Detection Approach,” in Proceedings of the ACM/IEEE Joint Conference on Digital Libraries, Institute of Electrical and Electronics Engineers Inc., May 2018, pp. 131–140. doi: 10.1145/3197026.3197042.

Praveen Krishnan, Computer Vision – ECCV 2016, vol. 9905. in Lecture Notes in Computer Science, vol. 9905. Cham: Springer International Publishing, 2016. doi: 10.1007/978-3-319-46448-0.

S. Appalaraju and V. Chaoji, “Image similarity using Deep CNN and Curriculum Learning.” Accessed: Jul. 07, 2023. [Online]. Available: https://arxiv.org/abs/1709.08761

E. Al-Thwaib, B. H. Hammo, and S. Yagi, “An academic Arabic corpus for plagiarism detection: design, construction and experimentation,” International Journal of Educational Technology in Higher Education, vol. 17, no. 1, Dec. 2020, doi: 10.1186/s41239-019-0174-x.

D. K. Mishra, R. Sheikh, S. Jain, and Institute of Electrical and Electronics Engineers, Apparel Classification Using Convolutional NeuralNetworksEshwar. 2016. doi: 10.1109/ICTBIG.2016.7892641.

N. Azahro Choirunisa, T. Karlita, and R. Asmara, “Deteksi Ras Kucing Menggunakan Compound Model Scaling Convolutional Neural Network,” Technomedia Journal, vol. 6, no. 2, pp. 236–251, 2021, doi: 10.33050/tmj.v6i2.1704.

H. Fonda, “Klasifikasi Batik Riau Dengan Menggunakan Convolutional Neural Networks (Cnn),” Jurnal Ilmu Komputer, vol. 9, no. 1, pp. 7–10, 2020, doi: 10.33060/jik/2020/vol9.iss1.144.

A. P. Song, Q. Hu, X. H. Ding, X. Y. Di, and Z. H. Song, “Similar Face Recognition Using the IE-CNN Model,” IEEE Access, vol. 8, pp. 45244–45253, 2020, doi: 10.1109/ACCESS.2020.2978938.

T. Xie, K. Wang, R. Li, and X. Tang, “Visual robot relocalization based on multi-task CNN and image-similarity strategy,” Sensors (Switzerland), vol. 20, no. 23, pp. 1–20, Dec. 2020, doi: 10.3390/s20236943.

K. E. Ak, J. H. Lim, J. Y. Tham, and A. A. Kassim, “Efficient multi-attribute similarity learning towards attribute-based fashion search,” in Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018, Institute of Electrical and Electronics Engineers Inc., May 2018, pp. 1671–1679. doi: 10.1109/WACV.2018.00186.

A. T. Prihatno, N. Suryanto, S. Oh, T. T. H. Le, and H. Kim, “NFT Image Plagiarism Check Using EfficientNet-Based Deep Neural Network with Triplet Semi-Hard Loss,” Applied Sciences (Switzerland), vol. 13, no. 5, 2023, doi: 10.3390/app13053072.




DOI: https://doi.org/10.24167/proxies.v6i1.12454

Copyright (c) 2024 Proxies : Jurnal Informatika



View My Stats