Intelligent Image Rights Protection System Using Machine Learning, Cloud Services and IoT Alerts
DOI:
https://doi.org/10.51903/jtie.v5i1.477Keywords:
Digital Image Protection, IoT-Based Monitoring, Real-Time Detection, Perceptual Hashing, Image Tamper DetectionAbstract
The rapid growth of digital media platforms has intensified the misuse of images through unauthorized manipulation, morphing, and non-consensual redistribution, posing significant threats to individual privacy and intellectual property rights. Despite the availability of reporting and takedown mechanisms, their effectiveness remains limited due to procedural complexity, delayed response times, and concerns regarding user anonymity. This paper presents the Smart Image Rights Protection (SIRP) system, a user-centric framework designed to detect, monitor, and respond to unauthorized use of images in online environments. The proposed system utilizes perceptual hashing to generate resilient digital fingerprints for registered images, enabling accurate identification after common transformations such as resizing, cropping, and minor visual alterations. A cloud-ready similarity analysis module is designed to support scalable matching in future deployments, while the current evaluation is conducted on a controlled dataset. An IoT-enabled hardware interface provides real-time alerts to users upon detection of potential misuse. Experimental results on controlled manipulation scenarios show that SIRP achieves detection accuracy of 95.6% for resized images and 94.3% for cropped images, outperforming traditional pixel-based comparison methods. Furthermore, automated evidence logging and instant notifications substantially reduce the latency between detection and user response actions. By combining robustness under common transformations, cloud-assisted processing, and timely user engagement, SIRP offers a practical solution for protecting digital image ownership and personal privacy.
References
Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., & Serra, G. (2011). A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery. IEEE Transactions on Information Forensics and Security, 6(3), 1099–1110. https://doi.org/10.1109/tifs.2011.2129512
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Communications Surveys & Tutorials, 17(4), 2347–2376. https://doi.org/10.1109/comst.2015.2444095
Asmaraloka, A. M., Hermansyah, M. A., Nisa, K., Saputra, F. H. D., & Setiawan, A. (2025). Implementation of the K-Nearest Neighbor (KNN) Algorithm in Handwritten Digit Pattern Recognition Using the Zoning Method. Jurnal Ilmiah Sistem Informasi, 4(2), 175–185. https://doi.org/10.51903/kf6s5f56
Bandyopadhyay, D., & Sen, J. (2011). Internet of Things: Applications and Challenges in Technology and Standardization. Wireless Personal Communications, 58, 49–69. https://doi.org/10.1007/s11277-011-0288-5
Bayram, S., Sencar, H. T., & Memon, N. (2009). An Efficient and Robust Method for Detecting Copy–Move Forgery. In Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1053–1056. https://doi.org/10.1109/icassp.2009.4960267
Buyya, R., Yeo, C. S., & Venugopal, S. (2008). Market-Oriented Cloud Computing: Vision, Architecture, and Challenges. In Proceedings of the 2008 IEEE International Symposium on Cluster Computing and the Grid (CCGRID), 5–13. https://doi.org/10.1109/ccgrid.2008.18
Ding, K., Meng, F., Liu, Y., Xu, N., & Chen, W. (2018). Perceptual Hashing Based Forensics Scheme for the Integrity Authentication of High Resolution Remote Sensing Image. Information, 9(9), 229. https://doi.org/10.3390/info9090229
Farid, H. (2009). Image Forgery Detection. IEEE Signal Processing Magazine, 26(2), 16–25. https://doi.org/10.1109/msp.2008.931079
Guo, Y., Dang, H., Fu, C., & Zhang, L. (2021). Deepfake Detection With Semantic Inconsistencies. In Proceedings of the 16th European Conference on Computer Vision (ECCV 2020), 445–461. https://doi.org/10.1007/978-3-030-58577-8_29
Kaur, P., & Singh, J. P. (2017). Image Copy-Move Forgery Detection Using SURF and DCT Features. In Proceedings of the 2017 IEEE International Conference on Advanced Computing and Communication Systems (ICACCI), 1–6. https://doi.org/10.1109/icacci
Li, C.-T. (2006). Identification of Bitmap Images Using Perceptual Hash. Journal of Systems and Software, 81(6), 971–980. https://doi.org/10.1016/j.jss.2007.01.018
Lu, C.-S., & Lin, C.-H. (2024). Robust Image Deepfake Detection With Perceptual Hashing. In Proceedings of the 10th International Conference on Information Systems Security and Privacy (ICISSP 2024), 220–229. https://doi.org/10.5220/0012317800003648
Masood, M., Nawaz, M., Malik, K. M., & Javed, A. (2021). Deepfakes Generation and Detection: A Survey. IEEE Access, 9, 98795–98817. https://doi.org/10.1109/access.2021.3099369
Mirsky, Y., & Lee, W. (2021). The Creation and Detection of Deepfakes: A Survey. ACM Computing Surveys, 54(1), 1–41. https://doi.org/10.1145/3425780
Miorandi, D., Sicari, S., De Pellegrini, F., & Chlamtac, I. (2012). Internet of Things: Vision, Applications and Research Challenges. Ad Hoc Networks, 10(7), 1497–1516. https://doi.org/10.1016/j.adhoc.2012.02.016
Nguyen, T. T., Nguyen, C. M., Nguyen, D. T., & Nahavandi, S. (2022). Deep Learning for Deepfakes Creation and Detection. Computer Vision and Image Understanding, 223, 103525. https://doi.org/10.1016/j.cviu.2022.103525
Pebadja, G., & Kholifah, S. (2023). The Impact of Brand Image, Pricing Strategies, and Product Quality on Consumer Loyalty in the Coffee Industry: An Empirical Study Using Structural Equation Modeling. Journal of Management and Informatics, 2(2), 1–20. https://doi.org/10.51903/jmi.v2i2.134
Popescu, A. C., & Farid, H. (2005). Exposing Digital Forgeries by Detecting Duplicated Image Regions. Dartmouth College Technical Report. https://farid.berkeley.edu/downloads/publications/cvpr05.pdf
Rössler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., & Nießner, M. (2020). FaceForensics++: Learning to Detect Manipulated Facial Images. International Journal of Computer Vision, 128, 260–275. https://doi.org/10.1007/s11263-019-01281-8
Shahri, E., Pedreiras, P., & Almeida, L. (2022). Extending MQTT With Real-Time Communication Services Based on SDN. Sensors, 22(9), 3162. https://doi.org/10.3390/s22093162
Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., & Ortega-Garcia, J. (2020). Deepfakes and Beyond: A Survey of Face Manipulation. Information Fusion, 64, 131–148. https://doi.org/10.1016/j.inffus.2020.06.014
Verdoliva, L. (2020). Media Forensics and Deepfakes: An Overview. IEEE Journal of Selected Topics in Signal Processing, 14(5), 910–932. https://doi.org/10.1109/jstsp.2020.3002101
Verdoliva, L., & Cozzolino, D. (2018). Image Forgery Detection: A Survey. IEEE Access, 6, 13745–13759. https://doi.org/10.1109/access.2018.2816030
Wang, S. Y., Wang, O., Zhang, R., Owens, A., & Efros, A. A. (2020). CNN-Generated Images Are Surprisingly Easy to Spot. In Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 869–878. https://doi.org/10.1109/cvpr42600.2020.00871
Yin, C., Liu, J., & Zhou, C. (2019). Real-Time Monitoring System Design for IoT Devices. IEEE Internet of Things Journal, 6(2), 3198–3207. https://doi.org/10.1109/jiot.2018.2872701
Zauner, C. (2010). Implementation and Benchmarking of Perceptual Image Hash Functions. Upper Austria University of Applied Sciences. https://www.phash.org/docs/pubs/thesis_zauner.pdf
Zhou, F., Qiao, Y., & Li, Q. (2024). Real-Time Enhancement of Low-Light Images Using Generative Adversarial Networks (GANs). Journal of Technology Informatics and Engineering, 4(1), 1–10. https://doi.org/10.51903/jtie.v4i1.279
Zhao, G., & Cao, Z. (2016). Robust Image Perceptual Hashing With Local and Global Features. IEEE Transactions on Information Forensics and Security, 11(8), 1803–1814. https://doi.org/10.1109/tifs.2016.2561905
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