一种Petri网优化的验证码识别方法

A CAPTCHA Identification Method Optimized by Petri Net

针对AlexNet网络对验证码(CAPTCHA)多目标分类问题效果不理想、模型参数量与浮点数计算量过大的问题,提出一种基于Petri网优化的CAPTCHA识别方法。利用Petri网理论对AlexNet和DenseNet-BC建模,并通过所建模型优化网络结构和参数。同时,根据模型参数量与浮点数计算量的关系,提出超活性概念,对Petri-ANPP-net、Petri-ANPS-net、Petri-DNBC-net模型进行灵敏度分析。实验结果表明,经过Petri网优化后,Petri-ANPP-net模型的最高准确度为60.40%,且超活性较小,模型灵敏度较差,Petri-ANPS-net模型的最高准确度为97.50%,但超活性较小,模型灵敏度较差,Petri-DNBC-net模型的最高准确度达到99.24%,且超活性较大,模型灵敏度较高。说明Petri网能在一定程度上优化网络模型结构和参数,且超活性对于评价模型的灵敏度具有一定的优越性。

AlexNet does not perform well in the multi-target classification of verification codes due to the large number of parameters and heavy floating point computation.To address the problem,this paper proposes a CAPTCHA recognition method optimized by Petri net.The method uses the Petri net theory to model AlexNet and DenseNet-BC,and optimizes the network structure and parameters with the built models.At the same time,according to the relationship between the number of model parameters and the amount of floating point computation,the concept of hyperactivity is proposed.Then sensitivity analysis is carried out on Petri-ANPP-net,Petri-ANPS-net,and Petri-DNBC-net models.Experimental results show that after the Petri net-based optimization,the highest accuracy of the Petri-ANPP-net model is 60.40%,and its super activity as well as the model sensitivity is poor.The highest accuracy of the Petri-ANPS-net model is 97.50%,but its superactivity and the model sensitivity is poor.The highest accuracy of the Petri-DNBC-net model is 99.24%,and its superactivity as well as the model sensitivity is high.The results show that Petri net can optimize the network model structure and parameters to a certain extent,and the hyperactivity has certain advantages in evaluating the sensitivity of the model.