Research On Improving The Robustness Of Image Recognition With Multiple Features And Associative Memory

The 21 st century is the era of the Internet.Science and technology are booming,promoting the rapid progress of human society.In the Internet era of many research directions,artificial intelligence come to the fore,has become the hottest research project in recent years.All Internet companies,research laboratories,schools are actively engaged in research on artificial intelligence.Among them,in the field of image recognition,the convolutional neural network has become a favored network structure with its high accuracy.But Nguyen found in 2015 a fact that deep neural networks are easily tricked into classifying unidentifiable pictures with high confidence.Although deep neural networks have outstanding accuracy in face recognition and digital recognition,researchers can easily make use of gradient ascent to produce images that are completely unrecognizable by human eyes.However,the most advanced deep neural network models use 99.99 The confidence of% considers these images to be recognizable and gives their "classification." Specifically,in a convolutional neural network that has been fully trained and performed well with the MNIST database or ImageNet database,genetic algorithms or gradient ascending methods are used to generate inputs that allow the input images to be sorted to the corresponding labels with the highest confidence The images,you will find these images are not recognized by the human eye.In this paper,the principle and function of bidirectional associative memory neural network(BAM),convolutional neural network(CNN)and t-SNE are introduced firstly.Then a bidirectional associative memory neural network model based on convolutional neural network and t-SNE is proposed.In this model,the feature vectors extracted by CNN and t-SNE are sent to BAM for associative memory.When an eigenvector is input from one end of BAM,the other end will input the result of association and have some fault tolerance.The two features are used to check each other,and the similarity between the associative result eigenvector and the correct result eigenvector is judged,and the similarity similarity threshold n is set.Then the MNIST database and 100 deception data obtained by the gradient ascent method were used to test the model.The test results show that the accuracy of the CNN is very good at 99.8% without cheating the data,but with the cheating data test,all the data are classified as a single label with more than 98% confidence.In our model,the threshold is set to 0.25,only 8% of this part of the data is still divided into the corresponding label,the other data are identified as deceptive data.It can be seen that our model has obvious improvement in anti-fraud data and can better ensure the recognition robustness.