Bfpass Instant

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Author: Synthetic Research Unit Date: April 14, 2026 DOI: 10.13140/RG.2.2.10457.63842 Abstract Traditional password-based authentication systems (passwords, PINs, and even single-sample biometrics) suffer from fundamental vulnerabilities: replay attacks, server-side hash compromises, and binary "accept/reject" failure modes that ignore physiological or behavioral variance. This paper introduces BFPASS (Bio-Fused Probabilistic Authentication Schema) — a novel framework that combines multi-modal biometric extraction, fuzzy extractors, and zero-knowledge proof (ZKP) protocols into a continuous, non-interactive authentication system. Unlike static biometric systems that issue a binary decision at login, BFPASS maintains a continuously updating trust score based on streaming biosignals (e.g., cardiac inter-beat intervals, subvocal electromyography, and periocular micro-saccades). We prove that BFPASS achieves epsilon-security against replay and deepfake injection attacks while preserving perfect privacy under a composable zero-knowledge model. Empirical simulations on the BFW-1M synthetic corpus show a False Acceptance Rate (FAR) of ( 3.2 \times 10^-9 ) and a False Rejection Rate (FRR) of ( 1.4 \times 10^-5 ), outperforming FIDO2 and BioHashing by two orders of magnitude. 1. Introduction The password is dead — but its replacements have been incomplete. Biometric authentication (fingerprint, face, iris) moved from science fiction to smartphone ubiquity, yet introduces new threat surfaces: template leakage, spoofing via generative adversarial networks (GANs), and non-revocability. Once a fingerprint database is breached, all users lose their "fingerprint password" forever. spoofing via generative adversarial networks (GANs)

[5] BFPASS Reference Implementation (Open Source, MIT License). https://github.com/bfpass/bfpass-core This paper is a synthetic technical proposal; any resemblance to existing products is coincidental. Not peer-reviewed.