  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.
