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

TL;DR

This paper evaluates the vulnerability of face recognition biometric systems to the Fast Gradient Sign Method (FGSM) attack, demonstrating how adversarial techniques can significantly reduce model performance.

Contribution

It provides a systematic analysis of FGSM attack effectiveness on face recognition systems and assesses the impact of different parameters on attack success.

Findings

FGSM can substantially decrease face recognition accuracy.

Parameter tuning influences attack effectiveness.

Face recognition models are vulnerable to adversarial attacks.

Abstract

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.