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

TL;DR: An adversarial attack based on incremental learning techniques for unmanned scenes can retain information previously learned by the model and can renew the old model to learn new model, thereby continually adding small perturbation to legitimate examples.

Abstract: 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 843%