A New Perspective of Deep Learning Testing Framework: Human-Computer Interaction Based Neural Network Testing

Deep learning models have revolutionized various domains but have also raised concerns regarding their security and reliability. Adversarial attacks and coverage-based testing have been extensively studied to assess and enhance the dependability of deep neural networks. However, current research in this area has reached a state of stagnation. Adversarial attacks focus on exploiting vulnerabilities in models, while coverage-based testing aims to achieve comprehensive testing but overlooks application scenarios. Moreover, evaluating test cases solely based on their fault-revealing capability is insufficient. To address these limitations, we propose an innovative interdisciplinary framework that incorporates human-computer interaction methods in deep learning security testing. By considering the attributes of model application scenarios, we can design more effective test suites that intend to reveal the model's behavior across various scenarios, aiding in the identification of potential defects. Consequently, the test suite plays a crucial role in the testing process of deep learning models, contributing to the assurance of model robustness and reliability. Additionally, we establish a comprehensive evaluation metric for test suite quality, considering factors such as diversity and naturalness. This framework promotes reliable and secure deployment of deep learning models, fostering interdisciplinary collaboration between artificial intelligence and human-computer interaction.