| dc.description.abstract | The recent advancements in synthetic media generation technologies have brought
about various challenges in the reliability of facial biometric authentication systems in
security sensitive areas. This systematic review explores current studies pertinent to
deep learning-based solutions for analyzing compromised facial media, with special
attention paid to their adaptability for use in biometric authentication systems. A
structured analysis was applied to twenty-eight peer-reviewed studies published from
2020 to 2024, with special focus placed on solutions based on convolutional neural
networks, transformer-based networks, and liveness detection methods for facial anti spoofing. Results show that transformer-based solutions demonstrate outstanding
detection capability and resistance against intricate manipulation patterns, while
convolutional neural network-based solutions possess lower computational complexity
and adaptability for real-time authentication applications in biometric systems. Yet
both demonstrate shortcomings in generalization capability across varied data sets
and susceptibility to ever-advancing synthetic media generation technologies. Liveness
detection is recognized as a supplementary mechanism for enhancing security for these
systems despite increased complexity in implementation and infrastructure requirements.
This systematic review draws attention to existing knowledge gaps in current studies
for the development of more secure, efficient, and adaptable deep learning-based
facial biometric authentication systems sensitive to novel threats from synthetic media
generations. | en_US |
