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Adversarial examples are artificially crafted to mislead deep learning systems into making wrong decisions. In the research of attack algorithms against multi-class image classifiers, an improved strategy of applying category explanation to the generation control of targeted adversarial example is proposed to reduce the perturbation noise and improve the adversarial robustness. On the basis of C&W adversarial attack algorithm, the method uses Grad-Cam, a category visualization explanation algorithm of CNN, to dynamically obtain the salient regions according to the signal features of source and target categories during the iterative generation process. The adversarial example of non-global perturbation is finally achieved by gradually shielding the non-salient regions and fine-tuning the perturbation signals. Compared with other similar algorithms under the same conditions, the method enhances the effects of the original image category signal on the perturbation position. And it makes up for the shortcomings of the adversarial example algorithm in terms of interpretability and teaching intuitiveness. Experimental results show that the improved adversarial examples have higher PSNR. In addition, in a variety of different defense processing tests, the examples can keep high adversarial performance and show strong attacking robustness. [ABSTRACT FROM AUTHOR]

Today, with the rapid development of science and technology, the era of big data has been proposed and triggered reforms in all walks of life. Face recognition is a biometric recognition method with the characteristics of non-contact, non mandatory, friendly and harmonious, which has a good application prospect in the fields of national security and social security. With the deepening of the research on face recognition, small-scale face recognition has achieved good recognition results, but in the era of big data, the existing small-scale face recognition methods have gradually failed to meet the social needs, and how to get a good face recognition effect in the era of big data has become a new research hotspot. Based on this, this paper aims to optimize the existing face recognition algorithm, study the face recognition method driven by big data, and propose a deep learning multi feature fusion face recognition algorithm driven by big data. First, for the problem that 2DPCA (Two-dimensional Principle Component Analysis) can well extract the global features of the face under large samples, but the local features of the face are difficult to process, this paper uses the LBP (Local Binary Pattern, LBP) algorithm to extract the texture features of the face, and the extracted texture features are integrated with the global features extracted by 2DPCA to multi-feature fusion, so that the fused features can take into account both global and local features, and have better recognition results. Then using the obtained fusion features as input, training in a convolutional neural network, and measuring the similarity based on the feature vectors of the sample set and the training set after the training, can realize multi-feature fusion face recognition. Through the analysis of simulation experiments, it is found that, compared with the use of global features or local features alone, the fusion features obtained by multi-feature fusion of global features extracted by 2DPCA and local features extracted by LBP algorithm have better recognition effect in the big data environment. After convolutional neural network trains and recognizes this feature, a high recognition accuracy rate is obtained, which can show that the face recognition method designed in this paper has good application potential in the era of big data. In the background of big data, the accuracy of face recognition can reach more than 90%, which can meet the needs of society well. • This paper aims to optimize the existing face recognition algorithm. • This paper uses the LBP algorithm to extract the texture features of the face. • This method fuse local & global features of human face in big data background. • The accuracy of face recognition reaches more than 90%. [ABSTRACT FROM AUTHOR]