增强视觉变压器防御针对基于挤压激发模块的对抗性攻击的稳定性
YouKang Chang1, Hong Zhao1, Weijie Wang1
1School of Computer and Communication, Lanzhou University of Technology, LanZhou, GanSu, China.
PeerJ. Computer science
|June 22, 2023
概括
这项研究提高了视觉变压器 (ViT) 模型对抗对手攻击的稳定性. 在ViT中引入ResNet-SE模块
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 人工智能的人工智能
背景情况:
- 与卷积神经网络 (CNN) 相比,视觉变压器 (ViT) 模型在计算机视觉任务中表现出优异的性能.
- 对抗对手攻击的ViT模型的稳定性仍然是一个未被充分探索的领域.
- 敌对攻击对实际应用中的深度学习模型的可靠性构成重大威胁.
研究的目的:
- 调查视觉变压器模型在受到各种对抗性攻击时的稳定性.
- 通过集成ResNet-SE模块来增强ViT模型的对抗性稳定性.
- 改进ViT模型的特征提取能力,以更好地防御干扰.
主要方法:
- 该研究介绍了ResNet-SE模块,特别针对ViT架构中的Attention模块.
- ResNet-SE模块旨在强调突出的特征,并抑制无关紧要的信息,帮助提取关键特征.
- 增强的ViT模型与几个既定的对抗性攻击方法进行了评估,包括BIM,C&W,DeepFool,DI2FGSM和MDI2FGSM.
主要成果:
- 提出的防御方法的准确度达到19.812% (BIM),17.083% (C&W),18.802% (DeepFool),21.490% (DI2FGSM) 和18.010% (MDI2FGSM) 的准确度.
- 实验结果表明,模型对对抗性干扰的弹性得到了显著改善.
- 与其他一些现有模型相比,防御方法表现出优越的强度.
结论:
- 集成ResNet-SE模块有效地提高了视觉变压器模型的对抗性稳定性.
- 拟议的方法为开发更安全可靠的计算机视觉系统提供了一个有希望的方向.
- 进一步的研究可以探索这种防御机制在各种计算机视觉领域的应用.
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