HighSpeed Rail Operating Environment Recognition Based on Neural Network and Adversarial Training Neural network is one of the key technologies for deep learning. Experiments on some standard test datasets show that their recognition ability has reached the level of human beings. However, they are extremely vulnerable to adversarial examples, that is, adding some subtle perturbations to the input example can cause the model to give a wrong output with high confidence. In this paper, we propose a noncontact approach based on neural network and adversarial training to recognize the highspeed rail operating environment. We first built the environment dataset and trained neural network models to do the recognition. We found that our model had high prediction accuracy, but with poor security since it was easy to attack our model using Basic Iterative Methods (BIM). To improve its security, we performed adversarial training based on the adversarial training dataset we built. The evaluation experiments indicated that this approach could improve the security of our model at the same time ensuring the prediction accuracy on the original test dataset.

A LabelBased Approach for Automatic Identifying Adversarial Examples with Image Transformation Besides extraordinary results that deep neural networks are widely deployed in many fields such as computer vision, speech recognition, and natural language processing, in recent years deep neural networks have been recognized vulnerable to intentional modification of legitimate inputs called adversarial examples. These patterns are almost indistinguishable from AI models and human perception. Adversarial examplesu0027 concern is rising dramatically and is attracted by many researchu0027s apprehensiveness because of its tremendous impact. Unfortunately, until now there is none of the defenses has been shown to be very effective. In this paper, we introduce a new defense strategy against adversarial examples by using a labelbased endtoend system. Our proposed defense system can mostly distinguish adversarial samples and benign images without human intervention. We exploit the important role of spatial domain in adversarial attacks and proposing a stateoftheart method for detecting adversarial examples based on our observation. We evaluate our systemu0027s performance on a variety of standard benchmark datasets including MNIST and ImageNet. Our proposed method reached out detection rates in a range from 94.6% to 99.2% in many settings.