引导卷积神经网络对抗对抗输入的重新训练
Francisco Durán1,2, Silverio Martínez-Fernández2, Michael Felderer3,4
1Universitat Politècnica de Catalunya, Barcelona, Catalunya, Spain.
PeerJ. Computer science
|September 14, 2023
概括
使用不确定性指标进行深度学习模型的重新训练和从原始权重进行对抗性重新训练,可以提高对抗性输入的准确性和效率. 这种方法有助于数据科学家在没有大量数据生成的情况下减轻漏洞.
科学领域:
- 深度学习和人工智能安全.
- 计算机视觉和图像分类.
- 软件测试和模型稳定性.
背景情况:
- 深度学习模型容易受到对抗性输入的影响,导致错误的分类.
- 针对对立的例子重新训练模型对于强大的软件测试至关重要.
- 有效的再培训需要指导指标和最佳数据集配置.
研究的目的:
- 评估指导指标和再培训配置,以改善对抗攻击的卷积神经网络.
- 为数据科学家在图像分类任务中优化准确性,资源利用率和执行时间.
主要方法:
- 在五个图像分类数据集上的实证研究.
- 在重新培训期间评估了六个指导指标 (神经元覆盖范围,惊喜充分性,DeepGini,软max,随机).
- 我们比较了三种再培训配置:一级对抗性再培训,对抗性再培训和对抗性微调.
主要成果:
- 从原始模型重量进行对抗性重新训练,以不确定性指标为指导,产生了优越的模型性能.
- 这种配置优化了准确性,资源利用率和执行时间.
结论:
- 建议从原始权重进行不确定性指标和对抗性重新训练,以提高深度学习模型的稳定性.
- 强调了数据集大小对模型性能的影响,并建议有效的策略来缓解对抗性漏洞.
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