From unlocking personal smartphones to securing international borders, biometric technologies—such as facial recognition, iris scanning, and fingerprint analysis—have fundamentally transformed digital security. While these physical identifiers offer a significant upgrade over traditional passwords in both convenience and reliability, they are increasingly becoming targets for sophisticated adversaries. Attackers are continuously developing novel ways to exploit vulnerabilities, targeting both the physical sensors that capture data and the underlying machine learning algorithms that process it. This paper explores the current landscape of biometric vulnerabilities, detailing the specific tactics used to deceive these systems. Crucially, it critically evaluates current \\\"liveness detection\\\" protocols—the security layers built to distinguish a genuine, living user from a synthetic or spoofed input—to assess their real-world effectiveness against modern evasion techniques. Experimental simulations and comparative analyses are conducted to measure spoofing success rates, detection accuracy, and authentication error rates under multiple attack scenarios. Based on the findings, a hybrid biometric security framework is proposed that integrates multi-modal biometric verification, behavioral biometrics, and artificial intelligence–based anomaly detection. The proposed framework aims to improve resilience against both physical spoof artifacts and AI-driven adversarial attacks in modern biometric systems.
Ubhe et al. (Thu,) studied this question.