Optimizing Image Recognition Algorithms with Differential Privacy Integration

With the rapid advancement of artificial intelligence technology, image recognition has become a core task in the field of computer vision and is widely applied across various industries.Image recognition technology significantly improves work efficiency and decision accuracy through the automatic analysis and processing of image data.However, image data often contain a large amount of sensitive information, making privacy protection a crucial issue in the application of image recognition technology.Existing differential privacy techniques effectively prevent the leakage of sensitive information by introducing noise into data processing.However, when applied to image recognition, these techniques often lead to a decline in recognition performance.Additionally, current integration methods lack effective evaluation of prediction accuracy and stability when handling predictions from multiple models, affecting the reliability and accuracy of the final recognition results.This paper proposes a vision Transformer network model with differential privacy protection and designs an image recognition algorithm that integrates differential privacy.By incorporating differential privacy mechanisms, we aim to enhance image recognition performance while safeguarding privacy.Furthermore, we introduce an adaptive weighting method to fuse predictions from different models, further improving recognition accuracy and stability.Our research not only provides a novel solution for privacy protection in image recognition but also theoretically and practically verifies the feasibility and effectiveness of differential privacy techniques in real-world applications.This study holds significant academic and practical value.