The synchronization between two neural networks by mutual learning can be used to design the neural key exchange protocol. The critical issue is how to evaluate the synchronization without a weight vector. All existing methods have a delay in evaluating the synchronization, which affects the security of the neural key exchange. To evaluate the full synchronization of neural networks more timely and accurately, an improved method for evaluating the synchronization is proposed. First, the frequency that the two networks have the same output in previous steps is used for assessing the degree of them roughly. Second, the hash function is utilized to judge whether the two networks have achieved full synchronization precisely when the degree exceeds a given threshold. The improved method can find the full synchronization between two networks with no information other than the hash value of the weight vector. Compared with other methods, the full synchronization can be detected earlier by two communication partners which adopt the method proposed in this paper. As a result, the successful probability of geometric is reduced. Therefore, the proposed method can enhance the security of the neural exchange protocol.

中文翻译：

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一种评估神经密钥交换协议同步性的改进方法

通过相互学习，两个神经网络之间的同步可用于设计神经密钥交换协议。关键问题是如何在没有权重向量的情况下评估同步。现有的所有方法在评估同步性方面都有延迟，这会影响神经密钥交换的安全性。为了更及时，更准确地评估神经网络的完全同步性，提出了一种改进的同步性评估方法。首先，两个网络在先前步骤中具有相同输出的频率用于大致评估它们的程度。其次，使用哈希函数来判断当度数超过给定阈值时，两个网络是否已实现完全同步。改进的方法可以找到两个网络之间的完全同步，除了权向量的哈希值外，没有其他信息。与其他方法相比，采用本文提出的方法的两个通信伙伴可以更早地检测到完全同步。结果，降低了几何的成功概率。因此，所提出的方法可以增强神经交换协议的安全性。