Why is block-stitched real webcam signal still caught: the joins, or mixing people?
research/mimic-webcam-stitch
Why is block-stitched real webcam signal still caught? With 50-frame blocks random stitching gives AUROC 0.887, one person's windows only 0.687, join-matched 0.796, both 0.588 (= whole-window replay 0.597); at 5 frames all are caught (8 seeds).
What it found
Ceiling study on the DUX webcam expression signals: 100-frame windows stitched from real blocks of 5 to 50 frames, scored by a boosted-tree detector on 14 held-out people (8 seeds). With 50-frame blocks random stitching is caught at AUROC 0.887; blocks from one person only give 0.687; join-matched blocks 0.796; both together 0.588, equal to replaying whole windows (0.597). At 5 frames everything is caught (≥0.989). Mixing people is the larger giveaway, join mismatch smaller, and removing both only reaches the replay ceiling. Every frame is real, so this is not a generator. Stylised: one form-filling context, 46 people.
A figure from its own output
Paper, code and results
More in Adversarial human imitation
- How well do prompt-free detectors tell people from a plainly prompted LLM, and do they carry over to unseen kinds of text?How well do prompt-free detectors tell people from a plainly prompted LLM, and do they carry over to unseen kinds of text? Eight RAID families split by family: n-gram detector AUROC 0.93 and Binoculars-style 0.80 on held-out families, length at chance. Preliminary first step of track 11.
- 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).
- Can prompt search alone move a frozen 0.5B imitator past a prompted 0.5B detector, and which proposer does it best at equal budget?Can prompt search alone move a frozen 0.5B imitator past a prompted 0.5B detector? A stylised first run of the imitation game, four proposers at equal budget on two families: the starting detector is below chance, proposers cannot be ranked at this size, and length is a major confound.
- 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-loopagainst 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. - Does a prompt-free human-vs-machine detector hold up on context families it never saw?Does a prompt-free n-gram human-vs-machine detector hold up on context families it never saw? Detector side of
mimic-anycontextwith no LLM (model download was blocked): AUROC 0.934 on unseen families against 0.985 in-family, and 40 target items close most of the gap on recipes. Stylised: plain-prompt RAID text only. - Which features let a human-vs-machine text detector carry over to a context family it never saw: words, characters, function words, formatting or length?Which features let a human-vs-machine detector carry over to a context family it never saw? Leave-one-family-out AUROC with train-only vectorisers: words 0.931, characters 0.916, both 0.938, function words alone 0.797, surface formatting 0.687 (below chance on abstracts and recipes), length alone 0.155 to 0.885 depending on family. Stylised: one classifier, plain-prompt RAID text, no LLM.
All 41 Adversarial human imitation projects →
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.