Smart Clothing Identity Authentication and Network Data Security Protection Based on Image Recognition

Abstract

In the field of smart clothing identity authentication, the recognition accuracy of traditional image recognition methods in dynamic environments is not high, and the traditional RSA (Rivest-Shamir-Adleman) encryption method consumes too much computation, resulting in data transmission delays, which in turn causes data security issues. In response to the problems of traditional methods, this paper constructs an improved ViT (Vision Transformers) model. Based on the traditional ViT model, a multi-level feature extraction mechanism is introduced to improve image recognition accuracy, and a self-attention mechanism is used for global feature modeling to deal with the recognition difficulties caused by factors such as posture changes and lighting changes in dynamic environments. A high-quality image dataset is constructed based on the facial features of the wearer, and image enhancement techniques such as rotation, cropping, and brightness adjustment can be used for preprocessing. This paper adopts an algorithm based on elliptic curve cryptography (ECC) to reduce computational complexity and reduce the delay of data encryption transmission. The improved ViT model can be integrated with the ECC encryption module, and after collaborative optimization, the response speed and data security of the overall system can be improved. Experimental results show that the improved ViT model has a recognition accuracy of 0.8 and a recall rate of 0.78 at a facial posture of 90 degrees, and a recognition accuracy and recall rate of 0.82 and 0.8 respectively at a light intensity of 350 Lux. The maximum ECC encryption and decryption time are 6.8 seconds and 6.9 seconds respectively, with lower transmission delay. Regrading data security, the integrated ViT and ECC encryption system has no more than 13 data leaks under 10,000 attacks of four standard attack methods, including man-in-the-middle attack, replay attack, brute force attack and 51% attack. The experimental results prove the effectiveness of this paper's research on smart clothing identity authentication and network data security protection.