Blar i Institutt for informasjonssikkerhet og kommunikasjonsteknologi på forfatter "Bassit, Amina"
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Fast and Accurate Likelihood Ratio-Based Biometric Verification Secure Against Malicious Adversaries
Bassit, Amina; Hahn, Florian; Peeters, Joep; Kevenaar, Tom; Veldhuis, Raymond Nicolaas Johan; Peter, Andreas (Peer reviewed; Journal article, 2021)Biometric verification has been widely deployed in current authentication solutions as it proves the physical presence of individuals. Several solutions have been developed to protect the sensitive biometric data in such ... -
Hybrid biometric template protection: Resolving the agony of choice between bloom filters and homomorphic encryption
Bassit, Amina; Hahn, Florian; Veldhuis, Raymond Nicolaas Johan; Peter, Andreas (Peer reviewed; Journal article, 2022)Bloom filters (BFs) and homomorphic encryption (HE) are prominent techniques used to design biometric template protection (BTP) schemes that aim to protect sensitive biometric information during storage and biometric ... -
Hybrid biometric template protection: Resolving the agony of choice between bloom filters and homomorphic encryption
Bassit, Amina; Hahn, Florian; Veldhuis, Raymond Nicolaas Johan; Peter, Andreas (Peer reviewed; Journal article, 2022)Bloom filters (BFs) and homomorphic encryption (HE) are prominent techniques used to design biometric template protection (BTP) schemes that aim to protect sensitive biometric information during storage and biometric ... -
Transferability analysis of adversarial attacks on gender classification to face recognition: Fixed and variable attack perturbation
Rezgui, Zohra; Bassit, Amina; Veldhuis, Raymond Nicolaas Johan (Journal article; Peer reviewed, 2022) -
Transferability analysis of adversarial attacks on gender classification to face recognition: Fixed and variable attack perturbation
Rezgui, Zohra; Bassit, Amina; Veldhuis, Raymond Nicolaas Johan (Peer reviewed; Journal article, 2022)Most deep learning-based image classification models are vulnerable to adversarial attacks that introduce imperceptible changes to the input images for the purpose of model misclassification. It has been demonstrated that ...