Multi-level loss object tracking adversarial attack method based on spatial perception

Xu CHENG; Yingying WANG; Nianjie ZHANG; Zhangjie FU; Beijing CHEN; Guoying ZHAO

doi:10.11959/j.issn.1000-436x.2021208

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Multi-level loss object tracking adversarial attack method based on spatial perception

Correspondences | 更新时间：2024-06-05

- Multi-level loss object tracking adversarial attack method based on spatial perception
- Journal on Communications Vol. 42, Issue 11, Pages: 242-254(2021)
- 作者机构：
  
  1. 南京信息工程大学计算机学院、软件学院、网络空间安全学院，江苏南京 210044
  2. 南京信息工程大学数字取证教育部工程研究中心，江苏南京 210044
  3. 奥卢大学机器视觉与信号分析研究中心，奥卢 FI-90014
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61802058);The National Natural Science Foundation of China(61911530397);China Scholarship Council (CSC)(201908320175);China Postdoctoral Science Foundation(2019M651650);The Natural Science Foundation of Jiangsu Province(BK20201267);The Natural Science Foundation of Jiangsu Province(BK20200039)
- DOI：10.11959/j.issn.1000-436x.2021208
  CLC： TP391
- Online First：2021-11，
  
  Published：25 November 2021
- 稿件说明：
移动端阅览
Xu CHENG, Yingying WANG, Nianjie ZHANG, et al. Multi-level loss object tracking adversarial attack method based on spatial perception[J]. Journal on Communications, 2021, 42(11): 242-254.
DOI：

Xu CHENG, Yingying WANG, Nianjie ZHANG, et al. Multi-level loss object tracking adversarial attack method based on spatial perception[J]. Journal on Communications, 2021, 42(11): 242-254. DOI： 10.11959/j.issn.1000-436x.2021208.

摘要

针对现有的对抗扰动技术难以有效地降低跟踪器的判别能力使运动轨迹发生快速偏移的问题，提出一种高效的攻击目标跟踪器方法。首先，所提方法从高层类别和底层特征考虑设计了欺骗损失、漂移损失和基于注意力机制的特征损失来联合训练生成器；然后，将干净图像送入该生成器中，生成对抗样本；最后，利用对抗样本干扰目标跟踪器，导致目标运动轨迹发生偏移，降低跟踪精度。实验结果表明，所提方法在 OTB 数据集上达到了54%的成功率下降和70%的精确度下降，实现了复杂场景下对目标快速有效的攻击。

Abstract

In order to solve the problem that it is difficult for the existing adversarial disturbance techniques to effectively reduce the discrimination ability of the trackers and make the trajectory deviation rapidly

an effective object tracking adversarial attack method was proposed.First

deception loss

drift loss and attention mechanism-based loss was designed to jointly train generator based on the consideration of the high-level categories and the low-level features.Then

the clean image was sent to the trained generator to generate the adversarial samples that were used to interfere with the object trackers

which made the object trajectory deviation and reduced the tracking accuracy.Experimental results show that the proposed method achieves 54% reduction in success rate and 70% reduction in accuracy on OTB dataset

which can attack the object of tracking quickly in complex scenes.

关键词

Keywords

references

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