The Role of Artificial Intelligence in Providing People With Privacy: Survey


  • Salar Adil Raees Computer Science Department, College of Science, University of Baghdad, Iraq
  • Mohammed Al-Tamimi Computer Science Department - College of Science - University of Baghdad



Privacy, Face, Person, Methods, Dataset, AI


Images privacy involves assessing the amount of information leakage from images, assessing risks associated with identification, and examining controls on this information. It was discussed various types of protection available and most commonly used in providing privacy to a person in images, including single-stage and two-stage detection algorithms. The results of each algorithm are organized in detailed tables, and the [YOLO] algorithm expands on all versions. The paper also clarifies the dataset used for testing the algorithms and its relevance to achieving desired results. It presents a comprehensive understanding of the process of detecting persons in digital images and assesses various tools and algorithms for recognizing persons, faces, and identities. It added an extensive examination of the several methods used to identify persons in digital images, with a specific emphasis on safeguarding their privacy. The task at hand is assessing various face recognition and identification tools and algorithms, with a specific emphasis on those that exhibit superior accuracy and efficiency in presenting outcomes. The study concluded that using the yolov8 algorithm in conjunction with blurring techniques effectively conceals individuals' information in digital images while maintaining the integrity of the overall image. The research paper's implications and information can practically contribute to the development of algorithms for detecting and protecting people in digital images, as well as the development of applications in this field. Theoretically, it can enhance understanding of the process of detecting and protecting people, and potentially contribute to the development of new theories in the field of protection and discovery.


Download data is not yet available.


Abed, R. M., Abdulmalek, H. W., Yaaqoob, L. A., Altaee, M. F., & Kamona, Z. K. (2023). Genetic Polymorphism of TLR5 and TLR6 in Iraqi Patients with Heart Failure Disease. Iraqi Journal of Science, 64(4), 1662–1674.

Abood, Q. K. (2023). Predicting Age and Gender Using AlexNet. TEM Journal, 12(1).

Adarsh, P., Rathi, P., & Kumar, M. (2020). YOLO v3-Tiny: Object Detection and Recognition using one stage improved model. 2020 6th International Conference on Advanced Computing and Communication Systems, ICACCS 2020, March 2020, 687–694.

Amer, F., Ali, M., & Al-Tamimi, M. S. H. (2022). Face mask detection methods and techniques: A review. Int. J. Nonlinear Anal. Appl, 13(November 2021), 2008–6822.

Anushka, Arya, C., Tripathi, A., Singh, P., Diwakar, M., Sharma, K., & Pandey, H. (2021). Object Detection using Deep Learning: A Review. Journal of Physics: Conference Series, 1854(1).

Aribilola, I., Naveed, M., & Lee, B. (2022). DEMIS?: A Threat Model for Selectively Encrypted Visual Surveillance Data DEMIS?: A Threat Model for Selectively Encrypted Visual Surveillance Data. October, 1–17.

Bai, T. (2020). Analysis on Two-stage Object Detection based on Convolutional Neural Networkorks. Proceedings - 2020 International Conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2020, 321–325.

Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection.

Brunetti, A., Buongiorno, D., Trotta, G. F., & Bevilacqua, V. (2018). Computer vision and deep learning techniques for pedestrian detection and tracking: A survey. Neurocomputing, 300, 17–33.

Patel, S. C., & Salot, P. (2021). Survey on Different Object Detection and Segmentation Methods. Int. J. Innov. Sci. Res. Technol, 6(1), 608-611.?

Cheng, R. (2020). A survey: Comparison between Convolutional Neural Network and YOLO in image identification. Journal of Physics: Conference Series, 1453(1).

Chettri, S. K., & Borah, B. (2015). Anonymizing classification data for preserving privacy. Communications in Computer and Information Science, 536, 99–109.

Chun, L. Z., Dian, L., Zhi, J. Y., Jing, W., & Zhang, C. (2020). YOLOv3: Face detection in complex environments. International Journal of Computational Intelligence Systems, 13(1), 1153–1160.

Dhillon, A., & Verma, G. K. (2020). Convolutional neural network: a review of models, methodologies and applications to object detection. Progress in Artificial Intelligence, 9(2), 85–112.

Girshick, R. (2015). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, 2015 Inter, 1440–1448.

Gonzalez Dondo, D., Redolfi, J. A., Araguas, R. G., & Garcia, D. (2021). Application of Deep-Learning Methods to Real Time Face Mask Detection. IEEE Latin America Transactions, 19(6), 994–1001.

He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2020). Mask R-CNN. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(2), 386–397.

Hosny, K. M., Zaki, M. A., Hamza, H. M., Fouda, M. M., & Lashin, N. A. (2022). Privacy Protection in Surveillance Videos Using Block Scrambling-Based Encryption and DCNN-Based Face Detection. IEEE Access, 10(September), 106750–106769.

Huangfu, Z., & Li, S. (2023). Lightweight You Only Look Once v8: An Upgraded You Only Look Once v8 Algorithm for Small Object Identification in Unmanned Aerial Vehicle Images. Applied Sciences, 13(22), 12369.

Intelligence and Neuroscience, C. (2023). Retracted: Local Privacy Protection for Sensitive Areas in Multiface Images. Computational Intelligence and Neuroscience, 2023, 1–1.

Khaled, F., & Al-Tamimi, M. S. H. (2021). Plagiarism Detection Methods and Tools: An Overview. Iraqi Journal of Science, 62(8), 2771–2783.

Kouadra, I., Bouchra, M., Bekkouche, T., & Ziet, L. (2023). Encryption face area in color images using Chaotic Maps. International Conference on Pioneer and Innovative Studies, 1, 501–506.

Lander, K., Bruce, V., & Hill, H. (2001). Evaluating the Effectiveness of Pixelation and Blurring on Masking the Identity of Familiar Faces. Applied Cognitive Psychology, 15(1), 101–116.<101::AID-ACP697>3.0.CO;2-7

Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., & Wei, X. (2022). YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications.

Li, R., & Yang, J. (2018). Improved YOLOv2 Object Detection Model. International Conference on Multimedia Computing and Systems -Proceedings, 2018-May, 1–6.

Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014). Microsoft COCO: Common objects in context. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8693 LNCS(PART 5), 740–755.

Matsumura, R. Y. O., & Hanazawa, A. (2019). Human detection using color contrast-based histograms of oriented gradients. International Journal of Innovative Computing, Information and Control, 15(4), 1211–1222.

Mittal, P., Singh, R., & Sharma, A. (2020). Deep learning-based object detection in low-altitude UAV datasets: A survey. Image and Vision Computing, 104.

Murthy, C. B., & Hashmi, M. F. (2020). Real time pedestrian detection using robust enhanced YOLOv3+. Proceedings - 2020 21st International Arab Conference on Information Technology, ACIT 2020.

Nakachi, T., & Kiya, H. (2020). Privacy-preserving Pattern Recognition with Image Compression. 01–12.

Ning, C., Zhou, H., Song, Y., & Tang, J. (2017). Inception Single Shot MultiBox Detector for object detection. 2017 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2017, July, 549–554.

Optoelectronics, S. (2023). THE REAL TIME DETECTION OF TRAFFIC PARTICIPANTS USING YOLOV7 ALGORITHM Saldambe Rema N Marak, Vinodkumar Jadhav School of ECE, Dr. Vishwanath Karad MIT-WPU, Pune, India. 42(1), 1477–1485.

Padilla-López, J. R., Chaaraoui, A. A., & Flórez-Revuelta, F. (2015). Visual privacy protection methods: A survey. Expert Systems with Applications, 42(9), 4177–4195.

Pulfer, E. (2019). Different Approaches to Blurring Digital Images and Their Effect on Facial Detection. Computer Science and Computer Engineering.

Rahma, M. M., & Salman, A. D. (2022). Heart Disease Classification-Based on the Best Machine Learning Model. Iraqi Journal of Science, 63(9), 3966–3976.

Rakhmawati, L., Wirawan, & Suwadi. (2018). Image Privacy Protection Techniques: A Survey. IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2018-Octob(October), 76–80.

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-Decem, 779–788.

Shao, X., Wei, J., Guo, D., Zheng, R., Nie, X., Wang, G., & Zhao, Y. (2021). Pedestrian Detection Algorithm based on Improved Faster RCNN. IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2021, 1368–1372.

Shetty, A. K., Saha, I., Sanghvi, R. M., Save, S. A., & Patel, Y. J. (2021). A Review: Object Detection Models. 2021 6th International Conference for Convergence in Technology, I2CT 2021, 1–8.

Shetty, J., & Jogi, P. S. (2019). Study on different region-based object detection models applied to live video stream and images using deep learning. In Lecture Notes in Computational Vision and Biomechanics (Vol. 30). Springer International Publishing.

Shifa, A., Babar, M., Mamoona, I., Asghar, N., & Fleury, M. (n.d.). Lightweight Human Skin Encryption for Public Safety in Real-time Surveillance Applications.

Sukkar, M., Kumar, D., & Sindha, J. (2021). Real-Time Pedestrians Detection by YOLOv5. 2021 12th International Conference on Computing Communication and Networking Technologies, ICCCNT 2021, November.

Tahir, A., Ahmad Khalid, S. K., & Mohd Fadzil, L. (2023). Child Detection Model Using YOLOv5. Journal of Soft Computing and Data Mining, 4(1), 72–81.

Wang, J., Zhang, T., Cheng, Y., & Al-Nabhan, N. (2021). Deep learning for object detection: A survey. Computer Systems Science and Engineering, 38(2), 165–182.

Wang, W. (2020). Detection of panoramic vision pedestrian based on deep learning. Image and Vision Computing, 103, 103986.

Wen, Y., Liu, B., Ding, M., Xie, R., & Song, L. (2022). IdentityDP: Differential private identification protection for face images. Neurocomputing, 501, 197–211.

Weng, G. (2023). Real-time pedestrian recognition on low computational resources.

Wiratama, W., & Sim, D. (n.d.). A person detection in HEVC bitstream domain based on bits density feature and YOLOv3 framework.

Xiao, X., & Tao, Y. (2006). Anatomy: Simple and effective privacy preservation. VLDB 2006 - Proceedings of the 32nd International Conference on Very Large Data Bases, 139–150.

Yang, S., Luo, P., Loy, C. C., & Tang, X. (2016). WIDER FACE: A face detection benchmark. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-Decem, 5525–5533.

Yuan, D., Zhu, X., Mao, Y., Zheng, B., & Wu, T. (2019). Privacy-Preserving Pedestrian Detection for Smart City with Edge Computing. 2019 11th International Conference on Wireless Communications and Signal Processing, WCSP 2019, 1–6.

Zhang, Y. M., Hsieh, J. W., Lee, C. C., & Fan, K. C. (2022). Sfpn: Synthetic Fpn for Object Detection. Proceedings - International Conference on Image Processing, ICIP, 1316–1320.

Zhong, Y., & Deng, W. (2023). OPOM: Customized Invisible Cloak Towards Face Privacy Protection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3), 3590–3603.

Zhu, Q., Avidan, S., Yeh, M. C., & Cheng, K. T. (2006). Fast human detection using a cascade of histograms of oriented gradients. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2, 1491–1498.

Zurich, E. T. H., Perception, M., Semester, S., Bucher, M., & Weiss, V. (2021). 3D Human Pose Estimation from RGB Images. 5–8.




How to Cite

Raees, S., & Al-Tamimi, M. (2024). The Role of Artificial Intelligence in Providing People With Privacy: Survey. Journal of Applied Engineering and Technological Science (JAETS), 5(2), 813–829.