Abstract

With the development of economy and the advancement of science and technology, society's requirements for the accuracy of face recognition have gradually increased, and its dependence on face recognition technology has become stronger and stronger. When deploying a neural network in a high-security environment, it is usually easier to ignore the security threat caused by vulnerability. Fully analyze the vulnerability of traditional face recognition technology, and use the generated adversarial samples to design a novel eyeglass patch sample, which can successfully deceive the face recognition system based on convolutional neural network. In addition, the security of artificial intelligence can bring new opportunities for face recognition. Applying artificial intelligence technology to face recognition can effectively improve accuracy. The research purpose of this paper is to analyze the application of adversarial samples in face recognition based on the security of artificial intelligence in order to solve the problems of low security and high vulnerability in traditional face recognition.

Highlights

  • 1.1 Literature reviewIn recent years, a lot of researches on face recognition has been carried out in academia

  • Designing a novel bright glasses patch sample can successfully deceive a face recognition system based on a convolutional neural network [1]

  • Ma Long and others studied the application of FLDA in single-sample face recognition, and proposed to superimpose a large number of general samples with every single sample according to a certain ratio, increase the total number of training samples of each class, and can effectively use the FLDA method for features Extraction

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Summary

Literature review

A lot of researches on face recognition has been carried out in academia. Xu Tao carried out canonical correlation analysis and its application in face recognition, and considered that canonical correlation analysis (LPCCA) is a new algorithm that can solve a large number of nonlinear problems. It achieves the goal of solving nonlinear problems through a locally linear method. Liu Xiaojun and others proposed a new face recognition method based on the hidden Markov model This method uses singular value decomposition to extract facial image features as the observation sequence, which reduces the amount of data storage and calculation, and improves the recognition rate [5]. This paper studies the application of artificial intelligence security-adversarial samples in face recognition, which has very important practical significance

Purpose of research
Adversarial sample
Face recognition
Artificial Intelligence Security Analysis
GAN-based adversarial sample generation
Sample generation
Application of adversarial samples in face recognition
6.Conclusion
Full Text
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