提高视频识别模型的稳定性:稀疏的对抗性攻击和超越
Ronghui Mu1, Leandro Marcolino2, Qiang Ni2
1Department of Computer Science, University of Liverpool, Liverpool, UK.
概括
DeepSAVA为视频引入了稀疏的对抗性攻击,为关键添加了不可察觉的变化,以欺骗分类器. 这种方法实现了高攻击成功和可转移性,并通过对抗训练提高了模型的稳定性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 网络安全 网络安全
背景情况:
- 对图像的对抗性攻击已经得到了很好的研究,但对视频的对抗性攻击仍然在很大程度上未被探索.
- 在视频攻击场景中,现有的方法往往缺乏效率和人类不可察觉性.
研究的目的:
- 为视频提出DeepSAVA,一种新的稀疏对抗性攻击策略.
- 提高视频分类模型对抗对方攻击的稳定性.
主要方法:
- 深度SAVA集成了空间转换和添加性扰动在关键视频上.
- 使用贝叶斯优化 (BO) 进行关键框架识别和随机梯度下降 (SGD) 来产生扰动.
- 采用结构相似性指数 (SSIM) 来测量框架变化,优先考虑人类不可察觉性.
主要成果:
- DeepSAVA实现了最先进的攻击成功率 (在I3D模型上高达100%的单干扰) 和对抗性可转移性.
- 在生成的对抗视频中表现出高效率和人类不可察觉性.
- 与基于PGD的方法相比,拟议的对抗性培训框架显著提高了视频分类器的稳定性.
结论:
- 迪普萨瓦 (DeepSAVA) 提出了一种有效和高效的方法,用于生成稀疏的对抗性视频攻击.
- 开发的对抗性培训策略增强了视频分类模型的弹性.
- 这项研究为了解和防御基于视频的对抗性威胁开辟了新的途径.
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