  Biometric data, such as face images, are often associated with sensitive
information (e.g medical, financial, personal government records). Hence, a
data breach in a system storing such information can have devastating
consequences. Deep learning is widely utilized for face recognition (FR);
however, such models are vulnerable to backdoor attacks executed by malicious
parties. Backdoor attacks cause a model to misclassify a particular class as a
target class during recognition. This vulnerability can allow adversaries to
gain access to highly sensitive data protected by biometric authentication
measures or allow the malicious party to masquerade as an individual with
higher system permissions. Such breaches pose a serious privacy threat.
Previous methods integrate noise addition mechanisms into face recognition
models to mitigate this issue and improve the robustness of classification
against backdoor attacks. However, this can drastically affect model accuracy.
We propose a novel and generalizable approach (named BA-BAM: Biometric
Authentication - Backdoor Attack Mitigation), that aims to prevent backdoor
attacks on face authentication deep learning models through transfer learning
and selective image perturbation. The empirical evidence shows that BA-BAM is
highly robust and incurs a maximal accuracy drop of 2.4%, while reducing the
attack success rate to a maximum of 20%. Comparisons with existing approaches
show that BA-BAM provides a more practical backdoor mitigation approach for
face recognition.
