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C/R-PMGAN:用于可转移黑子攻击的双路径自主监督和补丁口罩生成对抗网络
Zihan Peng1, Yang Xu1, Sicong Zhang1
1School of Cyber Science and Technology, Guizhou Normal University, Guiyang, 550001, Guizhou, China; Guizhou Key Laboratory of NewGen Cyberspace Security, Guiyang, 550001, Guizhou, China.
概括
本研究介绍了C/R-PMGAN,这是一种新的算法,可以增强对抗性攻击. 它提高了攻击在各种模型中的效率和可转移性,这对于现实世界的应用至关重要.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 生成对抗网络 (GAN) 擅长生成数据,并用于黑子对抗攻击.
- 可转移的黑子攻击对于现实场景至关重要,但在传统方法中面临效率和过度匹配问题.
- 现有的基于GAN的方法在跨模型特征对齐方面扎.
研究的目的:
- 提出C/R-PMGAN,一种增强对抗示例可转移性和提高效率的算法.
- 解决传统的梯度式和现有的基于GAN的可转移攻击的局限性.
主要方法:
- 在区分器中引入了一项双路径自主监督任务 (对比学习和旋转分类),以实现强大的特征表示.
- 开发了一种渐变聚合策略,使用随机补丁掩盖来将干扰聚焦在共享的语义信息上.
- 专注于增强跨模型特征对齐和干扰效率.
主要成果:
- 在16种不同的黑盒模型中,C/R-PMGAN实现了最高的平均攻击成功率,包括视觉变压器和防御训练模型.
- 在发电效率方面表现优于最先进的可转移攻击.
- 与现有方法相比,证明了优越的干扰不知觉性.
结论:
- 在黑盒设置中,C/R-PMGAN有效地提高了对抗性攻击的可转移性和效率.
- 提出的方法导致更强大的特征表示和有针对性的扰动.
- 该算法对现实世界的对抗性攻击应用具有显著的前景.
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