Adversarial attack against scene recognition system for unmanned vehicles

Abstract

Unmanned scene recognition means that unmanned vehicles can collect environmental data from equipped sensors and make decisions through algorithms, in which deep learning has become one of key technologies. Especially, with the discovery of adversarial examples against deep learning, the research on offensive and defensive against adversarial examples illustrates that the deep learning model for unmanned scene recognition also has the safety vulnerability. However, as far as we know, few studies have tried to explore the adversarial example attack in this field. Therefore, we try to address this problem by generating adversarial examples againist scene recognition classification model through experiments. In addition, we also try to improve the adversarial model robustness by the adversarial training. Extensive experiments have been conducted, and experimental results show that adversarial examples have an efficient attack effect on the neural network for scene recognition.