Towards universal and sparse adversarial examples for visual object tracking

Jingjing Sheng, Dawei Zhang, Jianxin Chen, Xin Xiao, Zhonglong Zheng

Abstract: Highlights•We propose a universal and black-box adversarial example generation framework for video object tracking task.•Our USAE only requires changing a small part of the entire datasets, which is sparse and effective to attack the trackers.•We expand the USAE to Siamese trackers and beyond various types of trackers, demonstrating its generality and transferability.