Innovative Application of Generative Adversarial Networks in Creative Clothing Design System from the Perspective of Data Security

Abstract

This research elaborates on a new fashion design system that utilizes a deep learning approach with a built-in data security solution. The architecture is developed on a GAN base fortified with attention mechanisms for style transfer and pattern derivative while securing data through differential privacy (= 0.1) and secure aggregation protocols. The system is able to perform style transfer processing and pattern generation with the model accuracy degradation being within 2% and achieving transfer processing in less than 50 milliseconds and 100 milliseconds for 95% of queries. The performance during security testing showcased powerful protection with 99.9% accuracy during authentication and a 98.2% success rate on intrusion detection wherein 256-bit encryption standards were utilized. The system is able to withstand vigorous testing such as 256-unit tests and 128 integration tests and showcases 98.8% reliability and a 99.5% user interface success rate. Optimization of resources achieved 30% more than baseline implementation with a reduction in GPU memory consumption. This paper shows an innovative integration between data security and AI-aided fashion design, marking a milestone in industrial scale possibilities.