Why Speech Deepfake Detectors Won’t Generalize: The Limits of Detection in an Open World

Speech deepfake detectors are often evaluated on clean, benchmark-style conditions, but deployment occurs in an open world of shifting devices, sampling rates, codecs, environments, and attack families. This creates a “coverage debt” for AI-based detectors: every new condition multiplies with existing ones, producing data blind spots that grow faster than data can be collected. Because attackers can target these uncovered regions, worst-case performance (not average benchmark scores) determines security. To demonstrate the impact of the coverage debt problem, we analyze results from a recent cross-testing framework. Grouping performance by bona fide domain and spoof release year, two patterns emerge: newer synthesizers erase the legacy artifacts detectors rely on, and conversational speech domains (tele- conferencing, interviews, social media) are consistently the hardest to secure. These findings show that detection alone should not be relied upon for high-stakes decisions. Detectors should be treated as auxiliary signals within layered defenses that include provenance, personhood credentials, and policy safeguards.