Does fitting a webcam-signal generator to one person fool a detector that has seen that person?

What it found

Each of 46 DUX webcam-signal people (12 to 54 ten-second windows) is split in time: generators see the first half only, a boosted-tree detector separates the second half from generated windows (6 seeds). Drawing 50-frame blocks from the person's own earlier windows gives AUROC 0.654 (join-matched 0.565, whole-window replay 0.593); drawing them from other people gives 0.913 (0.833, 0.582), rising to 0.971 (0.959, 0.926) when the detector is also given the person's mean level and spread. A fitted parametric generator (window levels, AR(1), channel correlation, holds) is caught at 1.000 whether fitted on one person or on everyone. So person-specific style is most of what separates copies from people; a fitted generator is still far from it. Stylised: one form-filling context; copies are ceilings, not generators.

A figure from its own output

Paper, code and results

• README and method
• Experiments and results
• Tests

More in Adversarial human imitation

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• Do the file sizes and line counts of this repository follow Benford’s law?Do this repository's file sizes follow Benford's law? Not as a whole (byte MAD 0.018, nonconforming) because templated files repeat the same size thousands of times; counting each distinct size once gives acceptable conformity (MAD 0.0068).
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• Does the prompt-only imitation game settle or cycle, and does beating the latest detector mean beating the others?Does the prompt-only imitation game settle or cycle? Every imitator prompt from mimic-loop against every detector prompt (0.5B on both sides): pure equilibria below chance (reddit 0.396, reviews 0.292), no cycles at a noise margin, and text length tracks detector accuracy. Stylised, one loop seed; model-size question open.

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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.