Authors :
Barathkumar P.; Madhankumar S.; Baranidharan R.; Dr. G. Valarmathy; Niranjan R.
Volume/Issue :
Volume 11 - 2026, Issue 4 - April
Google Scholar :
https://tinyurl.com/mrzkbfk7
Scribd :
https://tinyurl.com/y5p4z2jc
DOI :
https://doi.org/10.38124/ijisrt/26apr2325
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
With the help of Smart Blur technology, the Mater-nal Control Extension provides real-time, enhanced protection
by blocking content with traditional styles (website blocking). This service blurs out unhappy content through Optical
Character Recognition (OCR) for language and Convolutional Neural Net-works (CNN’s) for images, allowing children to
browse safely without compromising on quality of content. Also, it utilises adaptive literacy in order to grow from feedback
entered from parents and comes with an fluently useable UI in the form of a dashboard; allowing parents to acclimate their
sludge position, view their child’s operation reports and admit cautions when their child attempts to pierce unhappy
content. Incipiently, this service is completely biddable with data and sequestration legislation meaning children can browse
safely, intimately and for their own purpose in a controlled digital terrain.
Keywords :
Parental Control, AI, Real-Time Content Blurring, OCR, CNN, Computer Vision, Data Privacy, Adaptive Learning, Child Safety.
References :
- A. Smith, B. Johnson, and C. Williams, “Effectiveness Analysis of URL-Based Content Filtering in Modern Digital Environments,” IEEE Transactions on Information Forensics and Security, vol. 14, no. 8, pp. 2015-2028, Aug. 2019.
- B. Johnson and R. Williams, “Application-Level Content Block-ing: Limitations and Alternative Approaches,” ACM Computing Surveys, vol. 53, no. 2, pp. 1-35, Mar. 2020.
- L. Chen, M. Zhang, and K. Liu, “Deep Learning Approaches for Explicit Content Detection in Digital Media,” IEEE Transactions on Multimedia, vol. 23, pp. 1847-1860, 2021.
- C. Rodriguez and D. Anderson, “OCR-Based Text Analysis for Real Time Content Filtering Applications,” Pattern Recognition Letters, vol. 156, pp. 89-96, Apr. 2022.
- M. Thompson, S. Kumar, and A. Patel, “Privacy-Preserving Content Classification Using Federated Learning Approaches,” IEEE Transactions on Privacy and Security, vol. 20, no. 3, pp. 445-459, Mar. 2023.
- V. Kumar and R. Patel, “Real-Time Image Classification on Mo-bile Devices: Optimization Strategies and Performance Analysis,” IEEE Transactions on Mobile Computing, vol. 22, no. 7, pp. 4123-4137, Jul. 2023.
- A. Howard, M. Sandler, G. Chu, L.-C. Chen, B. Chen, M. Tan, W. Wang, Y. Zhu, R. Pang, V. Vasudevan, Q. V. Le, and H. Adam, “Searching for MobileNetV3,” arXiv preprint arXiv:1905.02244, 2019.
With the help of Smart Blur technology, the Mater-nal Control Extension provides real-time, enhanced protection
by blocking content with traditional styles (website blocking). This service blurs out unhappy content through Optical
Character Recognition (OCR) for language and Convolutional Neural Net-works (CNN’s) for images, allowing children to
browse safely without compromising on quality of content. Also, it utilises adaptive literacy in order to grow from feedback
entered from parents and comes with an fluently useable UI in the form of a dashboard; allowing parents to acclimate their
sludge position, view their child’s operation reports and admit cautions when their child attempts to pierce unhappy
content. Incipiently, this service is completely biddable with data and sequestration legislation meaning children can browse
safely, intimately and for their own purpose in a controlled digital terrain.
Keywords :
Parental Control, AI, Real-Time Content Blurring, OCR, CNN, Computer Vision, Data Privacy, Adaptive Learning, Child Safety.