mlsanityData to 14 Oct 2026

Attack Analysis of Face Recognition Authentication Systems Using Fast Gradient Sign Method

arXiv 2203.05653 ↗First posted 10 Mar 2022cs.CV cs.AIcited by 0

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Abstract (arXiv)

Biometric authentication methods, representing the "something you are" scheme, are considered the most secure approach for gaining access to protected resources. Recent attacks using Machine Learning techniques demand a serious systematic reevaluation of biometric authentication. This paper analyzes and presents the Fast Gradient Sign Method (FGSM) attack using face recognition for biometric authentication. Machine Learning techniques have been used to train and test the model, which can classify and identify different people's faces and which will be used as a target for carrying out the attack. Furthermore, the case study will analyze the implementation of the FGSM and the level of performance reduction that the model will have by applying this method in attacking. The test results were performed with the change of parameters both in terms of training and attacking the model, thus showing the efficiency of applying the FGSM.

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References · 10

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  4. 2014Going Deeper with Convolutionscited by 4,241
  5. 2017Adversarial Examples: Attacks and Defenses for Deep Learningcited by 351
  6. 2019Efficient Decision-based Black-box Adversarial Attacks on Face Recognitioncited by 175
  7. 2019Biometric Face Presentation Attack Detection with Multi-Channel Convolutional Neural Networkcited by 79
  8. 2018Unravelling Robustness of Deep Learning based Face Recognition Against Adversarial Attackscited by 31
  9. 2019Detection of Face Recognition Adversarial Attackscited by 15
  10. 2020A Thorough Comparison Study on Adversarial Attacks and Defenses for Common Thorax Disease Classification in Chest X-rayscited by 4

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