Does giving a webcam-signal generator real heavy tails and bursts fool a detector?

What it found

Follow-up to mimic-webcam on DUX facial-coding scores of 46 people. Generators that take each channel's marginal from the training people (Gaussian copula through empirical quantiles; quiet/burst regimes) still separate from 14 held-out people at reference AUROC 1.000 (8 seeds). Matching marginals alone made level and spread easier to catch (0.99–1.00 vs 0.91 and 0.94 for the Gaussian baseline); restoring per-window level and spread brings them to 0.54 and 0.57, but extremes (0.995), holds (0.995) and autocorrelation (0.94) still give it away. A sixth generator that copies a real window’s whole value set puts level, spread, extremes and floor at chance (0.44–0.46), yet all-groups AUROC stays 0.998: holds, autocorrelation, roughness and cross-channel structure carry it. Stylised: hand-built generators, one context, post-hoc final variants.

Paper, code and results

• README and method
• Tests

More in Adversarial human imitation

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Other research topics

Proper scoring & elicitation28Effort & peer prediction9Verification games & slashing19Detection & tolerances14Markets as routers13Prediction-market design9Paying contributors8Decentralised training systems23Digital twins & sim-to-real164LLM chip design loops40Robot manufacturing loops53Ambient AI assistant46Self-replicating manufacturing34Simulation-first design64Engineered clothing45Task-specific AI accelerators40Algorithmic chip-design loops42Open-source purpose-built robots44The software-engineering loop43Adversarial human imitation41Open-source residential construction41Autonomous multi-angle capture34Sensing, imaging and 3D31Energy supply and dense energy storage39Robot societies among people31Agentic co-design of low-cost robots27Laundry-hanging robots25Machine learning on qualitative data23Robots on the building site27Civilizations & group psychology1History & human sciences1

Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.