An Adversarial Attack Based on Incremental Learning Techniques for Unmanned in 6G Scenes

With the development of artificial intelligence(AI), unmanned vehicles can relieve traffic jamming and decrease the risk of traffic accidents, where deep neural networks (DNNs) play an important role and have become one of the most critical technologies. Nevertheless, DNNs are still susceptible to adversarial examples. Even worse, they also show severe performance degradation when the system needs DNNs to learn new knowledge without forgetting the old one. As unmanned vehicles travel on the road, they need to frequently learn new categories and different representations. Learning all data after the new sample arrives will expend a lot of time and space. As a result, it will affect the deployment of artificial intelligence in unmanned scenes. In recent years, it has been observed that incremental learning technology can solve the above challenges. However, previously reported works mainly focused on batch learning. It is not clear how much impact the adversarial attack will have on the deep learning model when performing incremental learning tasks. This issue exposes the hidden safety risks of unmanned driving and increases discuss opportunities. Therefore, we propose an adversarial attack based on incremental learning techniques for unmanned scenes in this paper. Specifically, it can retain information previously learned by the model. At the same time, it can renew the old model to learn new model, thereby continually adding small perturbation to legitimate examples. A couple of experiments on the Pascal VOC 2012 dataset has been conducted, and the experiment results show that the adversarial attack based on incremental learning techniques has a higher attack success rate. Further, it can improve the successful attack rate by 8.43%.