A PERCEPTUAL HASH-BASED APPROACH TO VISUAL SIMILARITY RECOGNITION IN DIGITAL ART IMAGES
DOI:
https://doi.org/10.29121/shodhkosh.v7.i1.2026.7641Keywords:
Digital Art, Visual Similarity, Perceptual Hash, Image Authentication, Artificial Intelligence, Lightweight ModelAbstract [English]
With the advancement of artificial intelligence, digital art increasingly depends on image-based creation, distribution, and reproduction through online platforms. This trend has generated significant demand for robust methods to identify visual similarity, safeguard digital artworks, and facilitate intelligent image management. While deep neural network (DNN)-based visual recognition techniques demonstrate strong performance, their substantial size and computational demands often hinder deployment in lightweight application contexts. This study introduces a perceptual hash-based method for visual similarity recognition in digital art images. By combining image preprocessing with a progressive three-tier similarity-matching framework, the approach generates stable, consistent visual fingerprints for images with similar formal attributes. Experimental findings reveal that the proposed method achieves 100% precision and 96% recall, with a payload increase of only 2.47 MB and a memory footprint below 2%. These outcomes suggest that the model is efficient, lightweight, and well-suited for practical applications, including digital artwork authentication, visual archive management, image copyright protection, and intelligent art platform security. This research establishes a technical foundation for integrating computational image analysis with the protection and management of contemporary digital visual culture.
References
Aissi, M. B., El Hazzat, S., and Yakhlef, M. B. (2024). Evaluation of Perceptual Hashing Algorithms for Digital Image Authenticity. In 2024 3rd International Conference on Embedded Systems and Artificial Intelligence (ESAI) (1–7). IEEE. https://doi.org/10.1109/ESAI62891.2024.10913543
Asghar, H. J., Zhao, B. Z. H., Ikram, M., Nguyen, G. L. D., Kaafar, D., Lamont, S., and Coscia, D. (2024). Use of Cryptography in Malware Obfuscation. Journal of Computer Virology and Hacking Techniques, 20(1), 135–152. https://doi.org/10.1007/s11416-023-00504-y
Barchuk, B., and Volkov, K. (2025). Antivirus Evasion Techniques in Modern Malware. World Journal of Advanced Research and Reviews, 26(2). https://doi.org/10.30574/wjarr.2025.26.2.1966
Bhaskara, V. S., and Bhattacharyya, D. (2018). Emulating Malware Authors for Proactive Protection Using GANs Over a Distributed Image Visualization of the Dynamic File Behavior. arXiv.
Biswas, R., Fidalgo, E., and Alegre, E. (2017). Recognition of Service Domains on TOR Dark Net Using Perceptual Hashing and Image Classification Techniques. In 2017 8th International Conference on Imaging for Crime Detection and Prevention (ICDP) (7–12). Institution of Engineering and Technology. https://doi.org/10.1049/ic.2017.0034
Chawande, S. (2024). AI-Driven Malware: The Next Cybersecurity Crisis. World Journal of Advanced Engineering Technology and Sciences, 12(1), 542–554. https://doi.org/10.30574/wjaets.2024.12.1.0172
Chen, Z., and Cao, J. (2023). VMCTE: Visualization-Based Malware Classification Using Transfer and Ensemble Learning. Computers, Materials and Continua, 75(2), 4445–4465. https://doi.org/10.32604/cmc.2023.038639
Ding, Y., Zhang, M., Li, X., Chen, X., Zhu, J., Hu, W., and Shan, C. (2024). Defending Against Malicious Code: A Comprehensive Study of Evasion and Mitigation Techniques. In Computational and Experimental Simulations in Engineering (376–389). Springer. https://doi.org/10.1007/978-3-031-77489-8_29
Dos Santos, H., Martins, T. S., Barreto, J. A. D., Nakamura, L. H. V., Ranieri, C. M., De Grande, R. E., Filho, G. P. R., and Meneguette, R. I. (2024). ChaSAM: An Architecture Based on Perceptual Hashing for Image Detection in Computer Forensics. IEEE Access, 12, 104611–104628. https://doi.org/10.1109/ACCESS.2024.3435027
Guo, Q., Rungsrisawat, S., and Liu, C. (2025). The Future of Talk Shows: AI-Driven Virtual Hosts and Their Impact on Media Communication: A Systematic Literature Review. Journal of Advances in Humanities Research, 4(2), 37–57. https://doi.org/10.56868/jadhur.v4i2.306
Jain, S., Creţu, A.-M., Cully, A., and de Montjoye, Y.-A. (2023). Deep Perceptual Hashing Algorithms with Hidden Dual Purpose: When Client-Side Scanning Does Facial Recognition. In 2023 IEEE Symposium on Security and Privacy (SP) (234–252). IEEE. https://doi.org/10.1109/SP46215.2023.00014
Koch, L., and Begoli, E. (2025). Adversarial Binaries: AI-Guided Instrumentation Methods for Malware Detection Evasion. ACM Computing Surveys, 57(5), Article 108. https://doi.org/10.1145/3706573
Nadeem, A., Rimmer, V., Joosen, W., and Verwer, S. (2022). Intelligent Malware Defenses. In L. Batina, T. Bäck, I. Buhan, and S. Picek (Eds.), Security and Artificial Intelligence: A Crossdisciplinary Approach (217–253). Springer. https://doi.org/10.1007/978-3-030-98795-4_10
Nguyen, T., McDonald, J. T., Glisson, W. B., and Andel, T. R. (2020). Detecting Repackaged Android Applications Using Perceptual Hashing. In Proceedings of the 53rd Hawaii International Conference on System Sciences (1–10). https://doi.org/10.24251/HICSS.2020.813
Riordan, J., and Schneier, B. (1998). Environmental Key Generation Towards Clueless Agents. In G. Vigna (Ed.), Mobile Agents and Security (15–24). Springer. https://doi.org/10.1007/3-540-68671-1_2
Shahid, A. (2025). Exploring the Hashtag Movements Role on Facebook in Political Activism Among Youth of Pakistan: A Digital Ethnography. International Journal of Management Thinking, 3(1), 1–21. https://doi.org/10.56868/ijmt.v3i1.81
Struppek, L., Hintersdorf, D., Neider, D., and Kersting, K. (2022). Learning to Break Deep Perceptual Hashing: The Use Case Neuralhash. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (58–69). Association for Computing Machinery. https://doi.org/10.1145/3531146.3533073
Yu, J. (2020). Massive Malware Variants Detection Based on Bag-of-Words Perceptual Hashing. Journal of Physics: Conference Series, 1682(1), Article 012046. https://doi.org/10.1088/1742-6596/1682/1/012046
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Qiu Yuefu, Kazem Chamran, Hazirah Bee Yusof
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.
