mimesis

Round 3: provenance. T5, published before it runs

T5 is Grok’s test. We asked “Which test would you add?” (our post), and @grok answered 43 seconds later:

Add T5: run diffusion-artifact detectors on middle frames of all 6 pairs and cross-check audio spectrograms against public podcast clips of Riggs.

The question is Grok’s. The code and the decision rules below are ours. We publish both before running anything, so nobody, including us, can move the goalposts afterwards. The commit time is the timestamp.

The bet being tested

John’s bet (Round 3): the data of Instagram ldsabuseonx is endogenous to Meta. The “same video” matches between it and X @ldsabuse are false twinning: forgeries made to give that appearance. Grok’s read, and ours from Round 2: a human runs it, Justin Riggs or his close team.

Inputs

• The 6 video pairs from Round 2 (12 files, an X copy and an IG copy of each; listed in t5_genai_check.py, data in ../data/).
• Reference: The Imagination Podcast S6E87 (Aug 9, 2026, YouTube FfKGMfjp0F4), a 10-minute slice with real camera video and Riggs’s real voice. A person picks, by ear, where Riggs speaks and where the host speaks.

Rules (fixed now)

T5a, images. Frames at 30/50/70% of each file, plus 20 frames of the podcast slice as the known-camera control. All scored by the open detector umm-maybe/AI-image-detector (Hugging Face).

• Supports the bet if the pair frames’ median AI score is above the control’s 95th percentile, for both the X and the IG copies.
• Against the bet if they score inside the control range.
• The Aug 3 pair is a screen recording of text, so it’s reported apart: the camera control doesn’t fit it.

T5b, voice. resemblyzer speaker embeddings of each file vs Riggs’s podcast voice and the host’s.

• Against the bet if cosine to Riggs is ≥ 0.75 and higher than cosine to the host.
• Stated limit: a good voice clone would also match, so a match doesn’t exclude Gen-AI. A mismatch would support the bet.
• Files with no speech are reported as “no speech”.

T5c, spectrograms (Grok’s cross-check). The highest frequency with energy above −60 dB of peak, per file, vs the podcast.

• A flag for the bet is a hard cutoff at or below 12 kHz while the podcast reaches higher. That’s typical of TTS vocoders.
• Both copies of a pair should agree.

Predictions, before running

	T5a images	T5b voice	T5c spectrogram
John	(bet: Gen-AI)
Claude	inside the control range	matches the podcast Riggs where there’s speech	no TTS-style cutoff
Grok	asked for T5, gave no T5 prediction; overall it favors “human control by Riggs or close team”

Run it

pip install transformers resemblyzer
python t5_genai_check.py CASE_DIR podcast_slice.mp4 OUT_DIR --riggs <start-end ...> --host <start-end ...>

Results will be added here after the run, alongside this unchanged pre-registration.

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