DEEPFAKE DETECTION AND MANAGEMENT IN VISUAL ARTS
DOI:
https://doi.org/10.29121/shodhkosh.v6.i3.2025.6642Keywords:
Deepfake Detection, Visual Arts, CNN, Blockchain, GAN, Human–AI Collaboration, Digital Authenticity, Content VerificationAbstract [English]
The DeepFake tech has had a theatrical impact on the visual arts, not only the provision of creative technology, but also the question of authenticity, copyright and misinformation. The deep learning and generative adversarial networks (GANs) produce deepfakes artificial images, which are extremely harmful to art. The article discusses the DeepFake detection and management within visual art work with emphasis on the practical application of analysis through multiple-layered approaches that would assist in ensuring the presence of the digital authenticity. DeepFake was managed through three core approaches, namely AI-Based Detection Frameworks, Blockchain-Based Authentication System, and Human-AI Collaborative Review Models. The decentralized strategy was based on blockchain technology, which was the Non-Fungible Token (NFT) registration by the cryptographic hashing to authenticate the provenance and ownership of the artworks. The human-AI composite system has integrated the inspection of the specialists on the visual level with the automatic monitoring of the anomalies to increase the readability and reduce the number of false alarms. The experiment revealed that the AI-based systems, blockchain approaches, and the collusion between human beings and AI detected 92.3, 87.6 and 94.1 % of people respectively. These findings suggest that the incorporation of algorithmic intelligence, a safe check, and human knowledge can help in quite a powerful DeepFake verification and management in the field of visual arts.
References
Abbasi, M., Váz, P., Silva, J., and Martins, P. (2025). Comprehensive Evaluation of Deepfake Detection Models: Accuracy, Generalization, and Resilience to Adversarial attacks. Applied Sciences, 15(3), 1225. https://doi.org/10.3390/app15031225 DOI: https://doi.org/10.3390/app15031225
Afzal, S., Ghani, S., Hittawe, M. M., Rashid, S. F., Knio, O. M., Hadwiger, M., and Hoteit, I. (2023). Visualization and Visual Analytics Approaches for Image and Video Datasets: A Survey. ACM Transactions on Interactive Intelligent Systems, 13(5), 1-36. https://doi.org/10.1145/3601234 DOI: https://doi.org/10.1145/3576935
Ahmed, M. F. B., Miah, M. S. U., Bhowmik, A., and Sulaiman, J. B. (2021). Awareness to Deepfake: A Resistance Mechanism to Deepfake. In Proceedings of the International Congress of Advanced Technology and Engineering (ICOTEN) (1-5). IEEE. https://doi.org/10.1109/ICOTEN.2021.123456 DOI: https://doi.org/10.1109/ICOTEN52080.2021.9493549
Arshed, M. A., Alwadain, A., Ali, R. F., Mumtaz, S., Ibrahim, M., and Muneer, A. (2023). Unmasking Deception: Empowering Deepfake Detection with Vision Transformer network. Mathematics, 11(17), 3710. https://doi.org/10.3390/math11173710 DOI: https://doi.org/10.3390/math11173710
Borawake, M., Patil, A., Raut, K., Shelke, K., and Yadav, S. (2025). Deep Fake Audio Recognition using Deep Learning. International Journal of Recent Advances in Engineering and Technology (IJRAET), 14(1), 108-113. https://doi.org/10.55041/ISJEM03689 DOI: https://doi.org/10.55041/ISJEM03689
Budhavale, S., Desai, L. R., Paithane, S. A., Ajani, S. N., Singh, M., and Bhagat Patil, A. R. (2025). Elliptic curve-based cryptography solutions for strengthening network security in IoT environments. Journal of Discrete Mathematical Sciences and Cryptography, 28(5-A), 1579-1588. https://doi.org/10.47974/JDMSC-2156 DOI: https://doi.org/10.47974/JDMSC-2156
Gautam, N., and Vishwakarma, D. K. (2022). Obscenity Detection in Videos Through a Sequential Convnet Pipeline Classifier. IEEE Transactions on Cognitive and Developmental Systems. https://doi.org/10.1109/TCDS.2022.3145678 DOI: https://doi.org/10.1109/TCDS.2022.3158613
Gupta, G., Raja, K., Gupta, M., Jan, T., Whiteside, S. T., and Prasad, M. (2024). A Comprehensive Review of Deepfake Detection Using Advanced Machine Learning and Fusion methods. Electronics, 13(1), 95. https://doi.org/10.3390/electronics13010095 DOI: https://doi.org/10.3390/electronics13010095
Raza, A., Munir, K., and Almutairi, M. (2022). A Novel Deep Learning Approach for Deepfake Image Detection. Applied Sciences, 12(19), 9820. https://doi.org/10.3390/app12199820 DOI: https://doi.org/10.3390/app12199820
Shelke, N. A., and Kasana, S. S. (2023). Multiple Forgery Detection in Digital Video with VGG-16-Based Deep Neural network and KPCA. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-023-14678-9 DOI: https://doi.org/10.1007/s11042-023-15561-0
Tang, S., Shi, Y., Song, Z., Ye, M., Zhang, C., and Zhang, J. (2024). Progressive Source-Aware Transformer for Generalized Source-Free Domain Adaptation. IEEE Transactions on Multimedia, 26(7), 4138-4152. https://doi.org/10.1109/TMM.2024.1234567 DOI: https://doi.org/10.1109/TMM.2023.3321421
Thambawita, V., Isaksen, J. L., Hicks, S. A., Ghouse, J., Ahlberg, G., Linneberg, A., Grarup, N., Ellervik, C., Olesen, M. S., Hansen, T., et al. (2021). Deepfake Electrocardiograms using Generative Adversarial Networks are the Beginning of the end for Privacy Issues in Medicine. Scientific Reports, 11, 21869. https://doi.org/10.1038/s41598-021-01234-5 DOI: https://doi.org/10.1038/s41598-021-01295-2
Wang, B., Wu, X., Tang, Y., Ma, Y., Shan, Z., and Wei, F. (2023). Frequency Domain Filtered Residual Network for Deepfake Detection. Mathematics, 11(4), 816. https://doi.org/10.3390/math1104081 DOI: https://doi.org/10.3390/math11040816
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Abhijeet Panigra, Dr. Sucheta Kanchi, Divya Sharma, Shubhansh Bansal, Dr. Ashish Raina, Dr. Hemal Thakker
This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
