Pelluru, Prem Sai (2025) Deep learning applications in brand identity protection: A technical analysis. World Journal of Advanced Engineering Technology and Sciences, 15 (1). pp. 1194-1205. ISSN 2582-8266
![WJAETS-2025-0356.pdf [thumbnail of WJAETS-2025-0356.pdf]](https://eprint.scholarsrepository.com/style/images/fileicons/text.png)
WJAETS-2025-0356.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial Share Alike.
Abstract
This technical article explores deep learning applications for brand identity protection through visual content analysis, focusing specifically on convolutional neural networks in e-commerce environments. We present an empirically validated framework that integrates optimized CNN architectures, multi-modal feature engineering, and scalable system design to address counterfeit detection challenges in digital marketplaces. The framework achieves over 95% detection accuracy while maintaining sub-100ms latency in production environments. We address key technical challenges including visual variations handling and false positive mitigation, provide detailed performance metrics, and explore emerging approaches in self-supervised learning, few-shot learning, and federated systems that promise to further advance brand protection capabilities.
Item Type: | Article |
---|---|
Official URL: | https://doi.org/10.30574/wjaets.2025.15.1.0356 |
Uncontrolled Keywords: | Brand Protection; CNN Architecture; Deep Learning; E-Commerce Security; Visual Analysis |
Depositing User: | Editor Engineering Section |
Date Deposited: | 04 Aug 2025 16:09 |
Related URLs: | |
URI: | https://eprint.scholarsrepository.com/id/eprint/2896 |