Self-learning and explainable deep learning network toward the security of artificial intelligence of things

At present, the security of the Internet of things (IoT) has aroused great concern in artificial intelligence area, Artificial Intelligence of Things (AIoT) are widely used in various intelligent surveillance scenarios. However, due to the weak interpretability of the model and high data security risks, developing a robust and explainable deep learning network framework for scene understanding under AIoT is extremely difficult. In addition, the fusion of IoT and AI also poses several challenges. To solve these difficulties, we develop a self-learning and explainable deep learning network toward the security of AIoT. The constructed system contains video collection, upload and display as well as data analysis and early warning operation at the embedded device end, and automatically recognizes the behaviors of scene by our developed visual recognition algorithms. In addition, the cloud computing platform can be controlled through our developed network. Our developed visual recognition algorithms contribute to three aspects. First, we propose a lightweight reinforcement learning network model by extracting spatial-temporal feature of different behavior characteristic. Then, we propose a self-paced learning framework through fusing the deep reinforcement learning and transfer learning. Finally, we propose a multi-perspective deep transfer learning model to solve the problem of weak explanation of model. The experimental results show that our proposed model is able to provide high interpretability of model and outperforms the state-of-the-art methods.