The conventional approach to verification is a constant arms race: a search for a signal that AI cannot reproduce.
We believe this strategy is structurally flawed. It assumes a static technological advantage that never holds against rapid AI advancement.
That’s why we stopped looking for an "unfreezable signal." Instead, we built a framework around something more durable and foundational: Cost Asymmetry.
Introducing the Forgery Cost Framework:
🔹 Single AI-generated frame: Nearly Zero Cost 💸
🔹 + Temporal Consistency: 5× Cost Multiplier ⏳
🔹 + Cross-modal (Camera + IMU): 25× Cost Multiplier 📡
🔹 + Live Randomized Challenge: 100× Cost Multiplier 🎲
Each cost multiplier is structural. It exists because maintaining a consistent, believable lie across independent, heterogeneous evidence channels is exponentially harder and more computationally expensive than generating a single plausible output.
This is why CPS-0001 is engine-independent by design. You don’t need one perfect, proprietary sensor. You need an architecture where adding more evidence channels makes forgery economically impossible while verification remains cheap.
Our proof is in the protocol:
📊 Four evidence engines, same protocol:
✅ EE-001 (PES): Cohen's d = 2.1 (High effect size)
✅ EE-002 (Cross-modal): 100% temporal alignment across 316 trials
✅ EE-003 (Challenge-response): 60% pass rate, 200 trials
✅ VS-001 (Pipeline): Dual-engine pipeline, 93% pass rate
The goal isn't to stop all forgery. The goal is to make forgery not worth it.
We are open-sourcing the future of digital identity verification under Apache 2.0. Dive into the research and build with us.
🔗 Open protocol (Apache 2.0): https://lnkd.in/gFwsvM2Z
🌐 Research hub: thecontinuitylab.org
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