arXiv:2606.11505v1 Announce Type: cross Abstract: Biometric systems are increasingly deployed in security applications; however, they remain vulnerable to spoofing attacks, in which attackers exploit counterfeit biometric data to gain unauthorized access. This research evaluates the effectiveness of state-of-the-art machine learning models, MobileNetV2, DenseNet-121, Inception-v3, and Spoof Trace Disentanglement (STD) in detecting spoofing attacks within facial recognition systems. Using the CelebA-Spoof dataset, the study evaluates model effectiveness using metrics such as accuracy, precision
The increasing deployment of biometric systems in security applications necessitates advanced deep learning techniques to counter evolving spoofing threats.
This research is crucial for maintaining the integrity and trustworthiness of biometric security systems, which are foundational for secure access control in various sectors.
The continuous improvement in spoofing detection, driven by state-of-the-art machine learning models, enhances the resilience of biometric authentication against sophisticated attacks.
- · Cybersecurity firms
- · Biometric system developers
- · Organizations relying on biometric access
- · Deep learning researchers
- · Attackers/spoofers
- · Legacy biometric systems
Increased confidence in biometric security leads to broader adoption across critical infrastructure and personal devices.
The arms race between biometric spoofing and detection technologies accelerates, driving further innovation in both fields.
Enhanced biometric security could enable more seamless and pervasive identity verification, potentially impacting individual privacy and surveillance capabilities.
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Read at arXiv cs.AI