DRL: Dynamic rebalance learning for adversarial robustness of UAV with long-tailed distribution

Yancheng Sun, Yuling Chen, Peng Wu, Xuewei Wang, Qi Wang

Abstract: Adversarial robustness has attracted extensive studies in various fields by increasing the interpretability of deep learning and enhancing the understanding of neural network models. In realistic scenarios such as UAV control system, imbalanced datasets are a consensus. Therefore, how to solve the adversarial robustness on imbalanced datasets is a more and more inescapable problem in UAV control system. There have been some works on adversarial robustness on imbalanced datasets, which bring us a deeper understanding of vulnerability of deep neural networks and the generation of adversarial examples. To adjust the classification plane after training, the long-tailed robustness framework is usually designed to be multi-stage, and different classifiers are used in different stages, which can improve the robustness through multi learning. The existing methods are hardly considered to effectively handle the long-tailed robustness problem in UAV control system. To explore the intrinsic features of long-tailed robustness, we propose a one-stage robustness framework. First, we study different classifiers and propose a general cosine classifier. By changing the general cosine classifier adaptively, the model obtains a more robust classification. Then, we analyze the scalability of the focal loss and design a focal-margin loss. Finally, we design a category focus mobile learning strategy, obtained more robust features by changing the learning emphasis with this strategy. From this, we design a simple and efficient one-stage dynamic adversarial robustness method DRL under long-tailed distribution, which consists of an adaptive cosine classifier and a focal-margin loss under long-tailed mobile learning. The extended experiments demonstrate the superiority of our approach over other state-of-the-art methods, and the effectiveness of the designed module. This method can effectively solve the long-tailed robustness problem on UAV control system and other terminals.