Design and implementation of student work management system in the context of deep learning

Abstract

This paper aims to analyze and design the system’s functions by student work management requirements, focusing on the business and user modules. The security testing algorithm based on the deep learning model verifies the system’s operation, analyzes the adversarial sample attack in image and text scenarios, and uses a black-box-white-box attack algorithm and defense algorithm for system security testing. Perform environment testing, compatibility testing, data response performance testing, and instance model evaluation for implementing the student work management system. Less than 500ms is the response time, over 400 requests are processed per second, and the timeout response rate is below 5%. The security evaluation coefficient was 66.982 for Example Model I and 74.628 for Example Model II, showing the system has good loadability and security.

Keywords

• deep learning model
• counter sample attack
• attack algorithm
• defense algorithm
• student work management
• 65d17