SECURE AND TRANSPARENT MODEL DEPLOYMENT USING ADDRESSING VULNERABILITIES IN INCEPTION-RESNET FOR AUTONOMOUS ROBOTICS AND MANUFACTURING

The integration of Inception-ResNet into autonomous robotics and manufacturing systems has significantly enhanced decision-making and operational efficiency. However, vulnerabilities in the model, such as susceptibility to adversarial attacks and reduced performance in dynamic environments, pose critical challenges for secure and transparent deployment. Ensuring robustness and reliability is essential for avoiding costly errors and maintaining operational safety in high-stakes applications. This work presents a comprehensive framework for secure and transparent deployment of Inception-ResNet by addressing its vulnerabilities. The proposed approach involves incorporating an adversarial training pipeline, optimized for autonomous robotics scenarios, and a blockchain-based logging mechanism to enhance transparency and traceability. Additionally, performance optimization is achieved through hyperparameter fine- tuning and the integration of dropout layers to reduce overfitting. The model is evaluated on a benchmark robotics dataset comprising 50,000 samples, split into 70% training and 30% testing datasets, to assess its performance. The proposed framework demonstrates significant improvements in both security and performance metrics. The enhanced model achieves an accuracy of 96.4%, a 4.7% increase compared to the baseline, with a robustness score against adversarial attacks improving from 73.2% to 89.6%. Deployment transparency is reinforced through blockchain implementation, ensuring data integrity and reducing unauthorized model access attempts by 92%. These results underline the potential of the proposed framework to set a new standard for deploying Inception-ResNet in critical robotics and manufacturing applications.