Adversarial Example Generation Method Based on Style Transfer

YU Zhenhua; YIN Zheng; YE Ou; CONG Xuya

doi:10.7652/xjtuxb202407018

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Adversarial Example Generation Method Based on Style Transfer

更新时间：2025-07-28

- Adversarial Example Generation Method Based on Style Transfer
- Vol. 58, Issue 7, Pages: 191-202(2024)
- 作者机构：
  
  西安科技大学计算机科学与技术学院,西安,710054
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202407018
  CLC： TP399
- Online First：10 July 2024，
  
  Published：2024
- 稿件说明：
移动端阅览
YU Zhenhua, YIN Zheng, YE Ou, et al. Adversarial Example Generation Method Based on Style Transfer[J]. 2024, 58(7): 191-202.
DOI：

YU Zhenhua, YIN Zheng, YE Ou, et al. Adversarial Example Generation Method Based on Style Transfer[J]. 2024, 58(7): 191-202. DOI： 10.7652/xjtuxb202407018.

摘要

针对现有面向目标检测的对抗样本生成方法泛化能力弱的问题

提出了一种融合风格迁移的对抗样本生成方法。首先

提出一种新的对抗补丁生成方法

使用风格迁移方法将风格图像不同层次特征提取并融合

生成无明显物体特征且纹理丰富的对抗补丁; 然后

利用梯度类激活映射方法生成目标的特征热图

对目标不同区域在目标检测模型中的关键程度进行可视化表示; 最后

构建一种热图引导机制

引导对抗补丁在攻击目标的关键位置进行攻击以提高其泛化能力

生成最终对抗样本。在DroNet室外数据集上进行实验

结果表明:针对单阶段目标检测模型YOLOv5生成的对抗样本

采用所提方法计算得到的攻击成功率可达84.07%; 应用于攻击两阶段目标检测模型Faster R-CNN时

采用所提方法计算得到的攻击成功率仍保持在67.65%; 与现有的主流方法相比

所提方法生成的对抗样本攻击效果较好

且具有良好的泛化能力。

Abstract

In response to the limited generalization in existing object detection-oriented adversarial example generation methods

an adversarial example generation method based on style transfer is proposed. Firstly

a novel adversarial patch generation method is introduced

leveraging style transfer techniques to blend features extracted from different layers of style images. This process generates adversarial patches that lack distinct object features but are rich in texture. Subsequently

a gradient-based activation mapping method is employed to generate feature heatmaps for the target

visually illustrating the significance of different regions of the target within the object detection model. Finally

a heatmap-guided mechanism is established to guide the adversarial patch to attack critical positions of the target

thereby enhancing its generalization ability and generating the ultimate adversarial examples. The proposed method's performance is verified through experiments conducted on the DroNet outdoor dataset. The experimental results demonstrate that this method achieves a success rate of 84.07% in generating adversarial samples for the single-stage object detection model YOLOv5. When applied to attack the two-stage object detection model Faster R-CNN

the success rate remains at 67.65%. Compared to prevailing methods

the adversarial examples generated by the proposed method exhibit superior attack effectiveness and good generalization capabilities.

关键词

Keywords

references

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