松鼠的脆弱性SARS-CoV-2血统分配到对抗性攻击的攻击
Amiel Meiseles1, Yair Motro2, Lior Rokach1
1Dept. of Software and Information Systems Engineering, Ben Gurion University of the Negev, Beer Sheva, Israel.
Artificial intelligence in medicine
|December 2, 2023
概括
众所周知的SARS-CoV-2血统工具Pangolin易受敌对攻击的影响. 研究人员表明,轻微的遗传变化可以改变血统分配,可能掩盖疫情爆发.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 准确的SARS-CoV-2血统分配对于疫情应对至关重要.
- 广泛使用的工具Pangolin使用 pangoLEARN 机器学习模型进行血统分类.
- 众所周知,机器学习模型容易受到对抗性攻击.
研究的目的:
- 调查Pangolin工具的脆弱性,特别是它的 pangoLEARN 模型,以对抗攻击.
- 为了证明改变SARS-CoV-2血统分配与最小的遗传修改的可行性.
主要方法:
- 开发了一个针对 pangoLEARN 架构的攻击.
- 在SARS-CoV-2基因组序列中引入了小小的干扰 (2-3个基对变化).
- 评估了攻击在改变不同血统的血统分配的成功率.
主要成果:
- 鱼表现出对非VoC血统的对抗性攻击的高度脆弱性,成功率为0.98-1.
- 对使用非ML方法 (Usher和Scorpio) 的VoC血统的攻击基本上没有成功.
- 该研究强调了恶意使用的可能性,例如伪造或掩盖疫情爆发.
结论:
- 基于机器学习的工具,如 pangoLEARN,容易受到对手的操纵.
- 非机器学习方法为VoC谱系提供了对此类攻击的更强大的稳定性.
- 微生物基因组学开发人员必须解决这些漏洞,以确保数据完整性和可靠的流行病应对.
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