527 research outputs found

    A Black-box Attack on Neural Networks Based on Swarm Evolutionary Algorithm

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    Neural networks play an increasingly important role in the field of machine learning and are included in many applications in society. Unfortunately, neural networks suffer from adversarial samples generated to attack them. However, most of the generation approaches either assume that the attacker has full knowledge of the neural network model or are limited by the type of attacked model. In this paper, we propose a new approach that generates a black-box attack to neural networks based on the swarm evolutionary algorithm. Benefiting from the improvements in the technology and theoretical characteristics of evolutionary algorithms, our approach has the advantages of effectiveness, black-box attack, generality, and randomness. Our experimental results show that both the MNIST images and the CIFAR-10 images can be perturbed to successful generate a black-box attack with 100\% probability on average. In addition, the proposed attack, which is successful on distilled neural networks with almost 100\% probability, is resistant to defensive distillation. The experimental results also indicate that the robustness of the artificial intelligence algorithm is related to the complexity of the model and the data set. In addition, we find that the adversarial samples to some extent reproduce the characteristics of the sample data learned by the neural network model

    Prediction of traffic flow based on deep learning

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    Deep neural networks (DNNs) have recently demonstrated the capability to predict traffic flow with big data. Although existing DNN models can provide better performance than shallow models, it is still an open question to make full use of the spatio-temporal characteristics of traffic flows to improve performance. We propose a novel deep architecture combining CNN and LSTM for traffic flow (RCF) predictio. The model uses CNN to explore temporal correlation and LSTM to explore spatial correlation . Factors such as weather and historical period data are also added to the feature. Its advantage lies in making full use of the spatial-temporal correlation of traffic data and more comprehensively considered the impact of multiple related factors. Aiming at the difficult problem of obtaining spatial features, a feature selection method based on Random Forests is proposed. We use the gini score to represent the spatial connection between intersections to form a network graph constructed based on data.  The experimental results show that based on the random forest feature selection and RCF model, the accuracy of traffic prediction reaches 90%

    Superconducting properties of novel BiSe2_{2}-based layered LaO1βˆ’x_{1-x}Fx_{x}BiSe2_{2} single crystals

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    F-doped LaOBiSe2_{2} superconducting single crystals with typical size of 2Γ—\times4Γ—\times0.2 mm3^{3} are successfully grown by flux method and the superconducting properties are studied. Both the superconducting transition temperature and the shielding volume fraction are effectively improved with fluorine doping. The LaO0.48_{0.48}F0.52_{0.52}BiSe1.93_{1.93} sample exhibits zero-resistivity at 3.7 K, which is higher than that of the LaO0.5_{0.5}F0.5_{0.5}BiSe2_{2} polycrystalline sample (2.4K). Bulk superconductivity is confirmed by a clear specific-heat jump at the associated temperature. The samples exhibit strong anisotropy and the anisotropy parameter is about 30, as estimated by the upper critical field and effective mass modelComment: 5 pages, 5 figures, 2 tables, accepted for publication in Europhysics Lette
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