变压器视觉追踪器中的安全性:关于两个模型对抗性稳固性的案例研究
Peng Ye1,2,3, Yuanfang Chen1,2, Sihang Ma1,2
1School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
视觉跟踪中的变压器模型容易受到敌对攻击. 研究人员表明,精心设计的对抗性示例可以显著降低性能,白盒攻击的成功率超过90%.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 视觉对象跟踪对于自动驾驶系统至关重要.
- 使用自我注意的变压器模型越来越多地用于视觉跟踪.
- 在这个领域,变压器模型的安全漏洞尚未得到充分理解.
研究的目的:
- 调查基于变压器的视觉跟踪对抗对方攻击的安全性.
- 评估对抗性干扰对跟踪性能的影响.
- 量化不同攻击策略的有效性.
主要方法:
- 在视频序列上生成对抗性示例,考虑对时间运动.
- 实施对抗性攻击以降低基于变压器的视觉跟踪性能.
- 在基准数据集上进行了实验 (OTB100,VOT2018,GOT-10k).
主要成果:
- 敌对的例子有效地降低了基于变压器的视觉追踪器的性能.
- 白盒攻击显示出最高的有效性,超过90%的攻击成功率.
- 该研究证实了这些模型对安全威胁的脆弱性.
结论:
- 基于变压器的视觉跟踪系统容易受到敌对攻击.
- 安全评估对于安全关键的应用,如自动驾驶,至关重要.
- 需要进一步的研究来开发强大的防御机制来应对此类威胁.
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