计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 178-183.doi: 10.11896/jsjkx.210500039
吴子斌, 闫巧
WU Zi-bin, YAN Qiao
摘要： 近年来,深度学习已被广泛应用于计算机视觉问题中,并取得了卓越的成功。但研究人员发现神经网络容易受到添加微弱扰动的原始样本的干扰,导致模型给出一个错误的输出,这类输入样本称为“对抗样本”。目前已有一系列生成对抗样本的算法被提出。针对已有的对抗样本生成算法——映射式梯度下降算法(Projected Gradient Descent),提出了结合动量并采用新的损失函数的改进方法MPGDCW算法,以确保更新方向的稳定且避免不良局部最大值的出现,同时避免交叉熵损失函数可能出现的梯度消失情况。通过与包含3种架构4个鲁棒模型的实验,证实了所提MPGDCW算法具有更优的攻击效果和更强的攻击迁移性。
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