一种新的深度神经网络投毒攻击

Jung-Shian Li, Yen-Chu Peng, I. Liu, Chuan-Gang Liu
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引用次数: 1

摘要

在医疗保健领域,许多机器学习方案已经应用于图像内容数据集的分析。其中,深度神经网络(dnn)也被称为深度学习,备受关注。然而,如果深度神经网络被攻击者破坏,医疗诊断可能会错误,从而导致重大后果。最近,我们发现由于中毒数据集可能会发生一种新的针对dnn的中毒攻击。这种新的中毒攻击,类别多样化攻击,具有更好的麻痹dnn的能力。我们的性能实验表明,我们的分类多样化攻击实际上导致dnn的准确率大幅下降。我们希望这一发现可以帮助信息专家在未来提高医疗数据集的质量。
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A New Poisoning Attacks on Deep Neural Networks
In healthcare field, many machine learning schemes have been applied in analyzing image content dataset. Among them, deep neural networks (DNNs), also known as deep learning, catches much attention. However, if deep neural networks are compromised by the attacker, medical diagnosis may be wrong, which leads to vital result. Recently, we find a new poisoning attack on DNNs may possibly happens due to poisoning dataset. This new poisoning attack, Category Diverse attack, has better ability to paralyze DNNs. Our performance experiments show our Category diverse attack actually leads to large accuracy drop of DNNs. We hope this discovery can help the information experts can improve the medical dataset quality in the future.
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