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Facial Recognition Systems (FRS) have become one of the most viable biometric identity authentication approaches in supervised and unsupervised applications. However, FRSs are known to be vulnerable to adversarial attacks such as identity theft and presentation attacks. The master face dictionary attacks (MFDA) leveraging multiple enrolled face templates have posed a notable threat to FRS. Federated learning-based FRS deployed on edge or mobile devices are particularly vulnerable to MFDA due to the absence of robust MF detectors. To mitigate the MFDA risks, we propose a trustworthy authentication system against visual MFDA (Trauma). Trauma leverages the analysis of specular highlights on diverse facial components and physiological characteristics inherent to human faces, exploiting the inability of existing MFDAs to replicate reflective elements accurately. We have developed a feature extractor network that employs a lightweight and low-latency vision transformer architecture to discern inconsistencies among specular highlights and physiological features in facial imagery. Extensive experimentation has been conducted to assess Trauma’s efficacy, utilizing public GAN-face detection datasets and mobile devices. Empirical findings demonstrate that Trauma achieves high detection accuracy, ranging from 97.83% to 99.56%, coupled with rapid detection speeds (less than 11 ms on mobile devices), even when confronted with state-of-the-art MFDA techniques.